Systems and methods of hierarchical smart asset control application development and optimization

ABSTRACT

Systems and methods of a Hierarchical Smart Asset Control Application development and Integrated Smart Asset Control System optimization are disclosed. In various embodiments, the system may develop a Hierarchical Asset Control Application and corresponding control hardware requirements. This can be used to create an Integrated Smart Asset Control System in order execute various processes for a set of equipment elements. The smart assets associated with the system may utilize intelligent agents to balance operational constraints and operational objects in order to determine real-time optimized operational parameters for a process and implement the appropriate controls to facilitate achieving the improved operational objectives.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and benefit from the followingprovisional patent applications: (1) U.S. Provisional Application Ser.No. 62/354,667 titled “Cyber Physical Systems” filed on Jun. 24, 2016,(2) U.S. Provisional Application Ser. No. 62/240,742 titled“Architecture For Connecting Objects In The Industrial Internet OfThings” filed on Oct. 13, 2015, (3) U.S. Provisional Application Ser.No. 62/279,224 titled “System And Methods For Device To Enterprise LevelIntrinsic Control” filed on Jan. 15, 2016, (4) U.S. ProvisionalApplication Ser. No. 62/406,926 titled “Systems And Methods OfHierarchical Asset Control Application Development and Optimization”filed on Oct. 11, 2016. The entire contents of the aforementioned patentapplications are expressly incorporated by reference herein.

BACKGROUND

Existing process control systems evolved out of the pneumatic andelectronic analog control systems that preceded them. These earlycontrol systems were designed to provide process control functions for acontrol loop or a number of related control loops of a productionprocess in an autonomous or semi-autonomous, but coordinated manner,with the coordination often being achieved through the appropriatesettings of set points across the multiple controllers. As such thearchitecture of early control systems was aligned to the process flow ofthe production process with each control typically limited to thecontrol of a very small component of the overall production flow of theoperation. As a result, each controller could be reasonably applied tothe component of the operation it was responsible to control.

With the introduction of digital computer technology as the deliveryvehicle for process control, the potential scope of automaticcoordinated control increased dramatically. But in order to make theseemerging digital control systems market acceptable they were programmedto directly replicate the functionality and architecture of the analogsystems that preceded them. To accomplish this, one solution provided asoftware configuration environment for process control based on“software blocks” to perform the exact functions of the analog controlsystem components. As such, the control system architectures thatevolved after the initial introduction of digital technologies werealigned to the process flows of the production operation and thereforewere designed to be configured to control from a process-centricperspective. This alignment and architecture worked quite well for thelast 60 years and has been the basis of process control system softwaredesign ever since.

Initially, the job of configuring the process control functions in theseprocess-centric automation systems was effectively accomplished by theengineers in industrial plants who had configured the traditional analogcontrol systems. This was because the control software was designed toreplicate the functionality and components of analog control systems.The transfer of knowledge from analog to digital control was veryeffective.

The scope of control of many of the early digital control systems wastypically about the same scope that had been implemented in analogsystems. Perhaps, this was due to the fact that the specifications ofcontrol systems had been done with the thought being that an analogcontrol system would be utilized or perhaps it was due to the comfortlevel of the engineers experienced using analog systems. In any case, asdigital systems were introduced, the scope of control did not tend toexpand to the potential offered by digital control systems. During thisphase the limitations of process-centric designs did not become obvious.

Over time, as process control engineers became more proficient andcomfortable using digital process control systems, a natural tendencyarose to want to integrally control larger and larger operationaldomains with a single automation system. During the 1980s there was apush to control entire process units in a coordinated manner. Over time,a desire emerged to control entire plant areas, trains, and even entireplants utilizing a single coordinated control strategy. The belief wasthat the more unified and coordinated the control strategy could beacross larger operational domains, the more efficient the operationwould be. However, a significant limitation to the process-centricdesign of the automation systems surfaced that made implementation ofsuch a strategy not feasible. A process-centric perspective of a fewcontrol loops or, perhaps even an entire process unit seemed manageable,but as the scope increased the complexity increased exponentially. Aprocess-centric view over large operational domains presents complex andchallenging coordinated control challenges as illustrated by the examplepiping and instrument diagram 100 of a partial operation depicted inFIG. 1. In fact, the complexity of the process-centric perspective wasso extreme that only the industrial operations with huge centralengineering functions continued to move toward large domain coordinatedcontrol. Most industrial operations continued to control their plantsmuch in the same manner as they had during the analog control systemperiod. As a result, much of the potential benefit of moving from analogto digital control was not realized.

A new class of control system known as Enterprise Control System (ECS)was designed in an effort to enable large domain coordinated control bycombining the traditional digital control system technology with openindustrial software. Although the combined architecture was a stepforward, the process-centric approach to control strategy design stilllimited the effective scope of coordinated control strategies.

The process-centric design of traditional control software remained as apreferred way of developing control strategies by industrial andindustrial automation suppliers. When batch control strategies becameavailable, it helped simplify some of the complexities of the processcentric automation and control systems. Batch control strategies requirea low layer of process and logic control for the basic equipment andloops in a plant. But over this basic control is a higher level of batchcontrol in which the control is oriented to the process units, trains,areas and the products being produced in them. Although the batchcontrol strategies were much simpler than if they had been attemptedthrough the strict process centric perspective, the complexity levelassociated with engineering, operating and maintaining the automationand control system remained relatively high. Moreover, the batch controlstrategies also did not address various challenges related toreliability and integrity of the system, for example, in the event offailure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating various equipment elements associatedwith an industrial system.

FIG. 2A is a diagram illustrating various embodiments of the ANSI/ISA-88standard for describing lower level equipment elements and correspondingprocess/procedures groupings.

FIG. 2B is a diagram illustrating an industrial topology of an equipmentelements.

FIG. 2C is a diagram illustrating an extended example ANSI/ISA-88physical equipment hierarchy/packaging line.

FIG. 3A is a diagram illustrating components of a Cyber Physical System(CPS) in accordance with various embodiments of the present disclosure.

FIG. 3B is a diagram illustrating example components or controllermodules for an intelligent agent in a CPS associated with a smart assetor smart asset grouping in accordance with various embodiments of thepresent disclosure.

FIG. 3C is a diagram illustrating elements of a smart asset inaccordance with various embodiments of the present disclosure.

FIG. 4A is a diagram illustrating an extended control hierarchy forHierarchical Asset Control Application implementations of real-timeoperational constraint/objective controls in accordance with variousembodiments of the present disclosure.

FIG. 4B is a diagram illustrating components controlling real-timeprofitability of a Hierarchical Asset Control Application in accordancewith various embodiments of the present disclosure.

FIG. 4C is a diagram illustrating a simplified representation of amulti-objective optimization problem including the components of FIG.4B.

FIG. 4D is a diagram illustrating a real-time control loop to improveoperational profitability of a Hierarchical Asset Control Application inaccordance with various embodiments of the present disclosure.

FIG. 4E is a diagram illustrating example mechanisms of achievingcontrol of a Hierarchical Asset Control Application in accordance withvarious embodiments of the present disclosure.

FIG. 5A is a diagram illustrating an implementation of an IntegratedSmart Asset Control System (ISACS) in accordance with variousembodiments of the present disclosure.

FIG. 5B is a diagram illustrating an implementation of an IntegratedSmart Asset Control System in accordance with various embodiments of thepresent disclosure.

FIG. 6 is an example of industrial equipment elements that can becontrolled and integrated with a Hierarchical Asset Control Application(Hierarchical Asset Control Applications) and corresponding controlhardware to form an Integrated Smart Asset Control System in accordancewith various embodiments of the present disclosure.

FIG. 7 is a flow diagram illustrating a method of Hierarchical AssetControl Application and corresponding control hardware development andintegration to create an Integrated Smart Asset Control System inaccordance with various embodiments of the present disclosure.

FIG. 8A is a flow diagram illustrating determining a system equipmentlist for developing a Hierarchical Asset Control Application inaccordance with various embodiments of the present disclosure.

FIG. 8B is a flow diagram illustrating determining a smart asset listfor developing a Hierarchical Asset Control Application in accordancewith various embodiments of the present disclosure.

FIG. 8C is an illustration of an intelligent asset template used todetermine a smart asset list for developing a Hierarchical Asset ControlApplication in accordance with various embodiments of the presentdisclosure.

FIG. 8D is a flow diagram illustrating validation and simulation of aHierarchical Asset Control Application and corresponding controlhardware in accordance with various embodiments of the presentdisclosure.

FIG. 8E is a flow diagram illustrating hardware instantiation of aHierarchical Asset Control Application in accordance with variousembodiments of the present disclosure.

FIG. 9A is a diagram illustrating an example of an industrial processcell in accordance with various embodiments of the present disclosure.

FIG. 9B is a diagram illustrating equipment elements controlled by aHierarchical Asset Control System for a single industrial process cellin accordance with various embodiments of the present disclosure.

FIG. 10 is a diagram illustrating various aspects of an example ofprocessing a system equipment list to develop a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 11 is a diagram illustrating various aspects of an example ofdeveloping a hierarchy of intelligent agents for a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 12 is a diagram illustrating various aspects of an example ofsimulation and validation for a Hierarchical Asset Control Applicationand corresponding control hardware in accordance with variousembodiments of the present disclosure.

FIG. 13 is a diagram illustrating an example of a control system modelfor optimizing the objectives and dynamic constraints of a HierarchicalAsset Control Application in accordance with various embodiments of thepresent disclosure.

FIG. 14A is a block diagram illustrating determining a safety riskconstraint related to assets in a Hierarchical Asset Control Applicationin accordance with various embodiments of the present disclosure.

FIG. 14B is a logic flow diagram illustrating determining a safety riskconstraint related to assets in a Hierarchical Asset Control Applicationin accordance with various embodiments of the present disclosure.

FIG. 15A is a block diagram illustrating determining an environmentalrisk constraint related to assets in a Hierarchical Asset ControlApplication in accordance with various embodiments of the presentdisclosure.

FIG. 15B is a logic flow diagram illustrating determining anenvironmental risk constraint related to assets in a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 16A is a block diagram illustrating determining a reliability riskconstraint related to assets in a Hierarchical Asset Control Applicationin accordance with various embodiments of the present disclosure.

FIG. 16B is a logic flow diagram illustrating determining a reliabilityrisk constraint related to assets in a Hierarchical Asset ControlApplication in accordance with various embodiments of the presentdisclosure.

FIG. 17A is a block diagram illustrating determining a security riskconstraint related to assets in a Hierarchical Asset Control Applicationin accordance with various embodiments of the present disclosure.

FIG. 17B is a logic flow diagram illustrating determining a securityrisk constraint related to assets in a Hierarchical Asset ControlApplication in accordance with various embodiments of the presentdisclosure.

FIG. 18A is a block diagram illustrating normalizing constraints relatedto assets in a Hierarchical Asset Control Application in accordance withvarious embodiments of the present disclosure.

FIG. 18B is a logic flow diagram illustrating normalizing constraintsrelated to assets in a Hierarchical Asset Control Application inaccordance with various embodiments of the present disclosure.

FIG. 19A is a diagram illustrating a radar visualization technique forsimultaneously controlling multiple objectives and outcomes of aHierarchical Asset Control Application in accordance with variousembodiments of the present disclosure.

FIG. 19B is a diagram illustrating a radar visualization technique forsimultaneously controlling multiple constrains and operating profitoutcome of a Hierarchical Asset Control Application in accordance withvarious embodiments of the present disclosure.

FIG. 19C is a diagram illustrating intersection of dynamic constraintsto form an operational boundary and optimization point of a HierarchicalAsset Control Application in accordance with various embodiments of thepresent disclosure.

FIG. 20A is a block diagram illustrating risk constraint communicationstructure from asset to set, and set to unit, of a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 20B is a logic flow diagram illustrating risk constraintcommunication structure from asset to set, and set to unit, of aHierarchical Asset Control Application in accordance with variousembodiments of the present disclosure.

FIG. 21A is a block diagram illustrating asset control communicationstructure from unit to set, and set to asset, of a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 21B is a logic flow diagram illustrating asset controlcommunication structure from unit to set, and set to asset, of aHierarchical Asset Control Application in accordance with variousembodiments of the present disclosure.

FIG. 22 is a diagram illustrating an analytics view of a system forHierarchical Asset Control Application in accordance with someembodiments of the present disclosure.

FIG. 23 is a diagram illustrating various aspects of an example ofdeveloping a hierarchy of intelligent agents for a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 24 is a diagram illustrating various aspects of an example ofdeveloping a hierarchy of intelligent agents for a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 25 is a diagram illustrating various aspects of an example ofdeveloping a hierarchy of intelligent agents for a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 26 is a diagram illustrating various aspects of an example ofdeveloping a hierarchy of intelligent agents for a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

FIG. 27 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

DETAILED DESCRIPTION

Embodiments of a method of Hierarchical Asset Control Applicationprocess development may comprise, accessing an equipment list;identifying industrial equipment elements; selecting an intelligentasset template from an intelligent asset template library to instantiatean intelligent agent for an equipment element; populating the selectedtemplate with operational constraints and operational objective data;and connecting iteratively, the instantiated intelligent agent todevelop a Hierarchical Asset Control Application.

In some embodiments of the method, the populated selected intelligentasset template includes intelligent agent instantiation information,validation is performed on the Hierarchical Asset Control Application,simulation is performed on the Hierarchical Asset Control Application,corresponding control hardware requirements are based on the developedHierarchical Asset Control Application, the developed Hierarchical AssetControl Application and the corresponding control hardware requirementsare integrated with the equipment elements to create an Integrated SmartAsset Control System, and/or the Integrated Smart Asset Control Systemincludes more than one smart asset control level.

In other embodiments the method may further comprise, aggregating one ormore intelligent asset templates to instantiate an intelligent agent forincorporation with a smart asset, instantiating an intelligent assettemplate for a smart asset grouping for an intelligent asset template isinstantiated for a smart asset set, and/or the intelligent assettemplate is configured to include application specific data.

In other embodiments the method may further comprise, determining assetoperational library type and industry specific hierarchical controlapplication default requirements, the intelligent asset application isdeveloped as an intelligent agent for the particular equipment elementcontrol model, the intelligent asset template includes data parametersincluding, suggested asset interconnects with assets, operationalconstraints, operational objectives, high availability/criticalityparameter, or industry specific industrial applications, the intelligentasset template includes vendor device-specific model information, and/orthe intelligent asset template includes operational parameters fromgeneric device type models.

In other embodiments the method may further comprise, determiningoperational constraint parameters including reliability, environmental,or safety, determining operational objective parameters including energycost, materials cost, production value, or profitability, determiningoperational efficiency parameters, connecting iteratively includesgrouping related smart assets into smart asset groupings that defineparent/child control relationship across smart assets, and/or simulatingthe Hierarchical Asset Control Application involved generatingvirtualized equipment element data and executing process controlelements.

Embodiments of a system of Hierarchical Asset Control Applicationprocess development may comprise, accessing, with a processor, anequipment list; identifying, with the processor, industrial equipmentelements; selecting, with the processor, an intelligent asset templatefrom an intelligent asset template library to instantiate an intelligentagent for an equipment element; populating, with the processor, theselected template with operational constraints and operational objectivedata; and connecting iteratively, with the processor, the instantiatedintelligent agent to develop a Hierarchical Asset Control Application.

In some embodiments of the system, the populated selected intelligentasset template includes intelligent agent instantiation information,validation is performed on the Hierarchical Asset Control Application,simulation is performed on the Hierarchical Asset Control Application,corresponding control hardware requirements are based on the developedHierarchical Asset Control Application, the developed Hierarchical AssetControl Application and the corresponding control hardware requirementsare integrated with the equipment elements to create an Integrated SmartAsset Control System, and/or the Integrated Smart Asset Control Systemincludes more than one smart asset control level.

In other embodiments the system may further comprise, aggregating one ormore intelligent asset templates to instantiate an intelligent agent forincorporation with a smart asset, instantiating an intelligent assettemplate for a smart asset grouping for an intelligent asset template isinstantiated for a smart asset set, and/or the intelligent assettemplate is configured to include application specific data.

In other embodiments the system may further comprise, determining assetoperational library type and industry specific hierarchical controlapplication default requirements, the intelligent asset application isdeveloped as an intelligent agent for the particular equipment elementcontrol model, the intelligent asset template includes data parametersincluding, suggested asset interconnects with assets, operationalconstraints, operational objectives, high availability/criticalityparameter, or industry specific industrial applications, the intelligentasset template includes vendor device-specific model information, and/orthe intelligent asset template includes operational parameters fromgeneric device type models.

In other embodiments the system may further comprise, determiningoperational constraint parameters including reliability, environmental,or safety, determining operational objective parameters including energycost, materials cost, production value, or profitability, determiningoperational efficiency parameters, connecting iteratively includesgrouping related smart assets into smart asset groupings that defineparent/child control relationship across smart assets, and/or simulatingthe Hierarchical Asset Control Application involved generatingvirtualized equipment element data and executing process controlelements.

Other embodiments of a method of Hierarchical Asset Control Applicationprocess development may comprise, accessing an equipment list;identifying industrial equipment elements; selecting an intelligentasset template from an intelligent asset template library to instantiatean asset application model for an equipment element; populating theselected template with operational constraints and operational objectivedata; connecting iteratively, the instantiated asset application modelsto develop a Hierarchical Asset Control Application; and wherein thepopulated selected intelligent asset template includes intelligent agentinstantiation information.

Embodiments of a method of Integrated Smart Asset Control Systemoptimization, may comprise, accessing asset automation operationalprocess parameters associated for an asset within an Integrated SmartAsset Control System; developing a multi-faceted dynamic processconstraints accounting for operational constraints associated with theautomation operational process; evaluating a multi-faceted dynamicprocess constraint model to balance operational process constraints andprocess objectives; determining an optimized process operation point forthe multi-faceted dynamic process constraints; determining ahierarchical smart asset control action utilizing the optimized processoperation point; and executing the hierarchical smart asset controlaction to transition from current operational values to operationalvalues to achieve the balanced operational constraints.

In other embodiments the method may comprise, wherein the operationalconstraints are developed by a smart asset associated with a smart assetgroup within the Integrated Smart Asset Control System, the hierarchicalasset control actions effectuate control change of a smart assetassociated with the Integrated Smart Asset Control System, determiningan optimized process operation point is based on reliability, safety,and environmental process operational constraints, optimized processoperation point determination is achieved based on asset constraintaggregation across each smart asset in a smart asset grouping, thereliability constraint is determined for the smart asset set isdetermined utilizing a reliability risk model, the real time reliabilityconstraint is defined as: RT Reliability Risk=MAX (OperationalReliability Risk, Conditional Reliability Risk, Reliability SafetyRisk), the environmental constraint is determined for the smart assetset is determined utilizing an environmental risk model, the real timereliability constraint is defined as: RT Environmental Risk=MAX(Operational Environmental Risk, Conditional Environmental Risk,Reliability Environmental Risk), the safety constraint is determined forthe smart asset set is determined utilizing a safety risk model, thereal time reliability constraint is defined as: RT Safety Risk=MAX(Operational Safety Risk, Conditional Safety Risk, Reliability SafetyRisk), access to a smart asset is dynamically controlled based onaggregated real time security data, and/or the aggregated real timesecurity data includes both Integrated Smart Asset Control Systemspecific and external security data.

In other embodiments the method may comprise, determining an optimizedprocess operation point is derived utilizing linear analysis, thedevelopment of the multi-faceted process constraint is derived utilizingnon-linear analysis, the hierarchical smart asset control actioninstructs a parametric operational state for a smart asset or group ofsmart assets, the hierarchical smart asset control action instructs aparametric operational set point for a smart asset or group of smartassets, the hierarchical smart asset control action instructs aparametric operational constraint threshold for a smart asset or groupof smart assets, the accessing smart asset automation operationalprocess parameters associated with a hierarchical smart assetsystem/group is retrieved from a data store, and/or the operationalprocess parameters include historic or real time data.

In other embodiments the method may comprise, aggregating smart assetoperational data; and using smart asset operational data to improveefficiency or prospective process command selections.

Embodiments of a system of Integrated Smart Asset Control Systemoptimization, may comprise, accessing, with a processor, assetautomation operational process parameters associated for an asset withinan Integrated Smart Asset Control System; developing, with theprocessor, a multi-faceted dynamic process constraints accounting foroperational constraints associated with the automation operationalprocess; evaluating, with the processor, a multi-faceted dynamic processconstraint model to balance operational process constraints and processobjectives; determining, with the processor, an optimized processoperation point for the multi-faceted dynamic process constraints;determining, with the processor, a hierarchical smart asset controlaction utilizing the optimized process operation point; and executing,with the processor, the hierarchical smart asset control action totransition from current operational values to operational values toachieve the balanced operational constraints.

In other embodiments the system may comprise, wherein the operationalconstraints are developed by a smart asset associated with a smart assetgroup within the Integrated Smart Asset Control System, the hierarchicalasset control actions effectuate control change of a smart assetassociated with the Integrated Smart Asset Control System, determiningan optimized process operation point is based on reliability, safety,and environmental process operational constraints, optimized processoperation point determination is achieved based on asset constraintaggregation across each smart asset in a smart asset grouping, thereliability constraint is determined for the smart asset set isdetermined utilizing a reliability risk model, the real time reliabilityconstraint is defined as: RT Reliability Risk=MAX (OperationalReliability Risk, Conditional Reliability Risk, Reliability SafetyRisk), the environmental constraint is determined for the smart assetset is determined utilizing an environmental risk model, the real timereliability constraint is defined as: RT Environmental Risk=MAX(Operational Environmental Risk, Conditional Environmental Risk,Reliability Environmental Risk), the safety constraint is determined forthe smart asset set is determined utilizing a safety risk model, thereal time reliability constraint is defined as: RT Safety Risk=MAX(Operational Safety Risk, Conditional Safety Risk, Reliability SafetyRisk), access to a smart asset is dynamically controlled based onaggregated real time security data, and/or the aggregated real timesecurity data includes both Integrated Smart Asset Control Systemspecific and external security data.

In other embodiments the system may comprise, determining an optimizedprocess operation point is derived utilizing linear analysis, thedevelopment of the multi-faceted process constraint is derived utilizingnon-linear analysis, the hierarchical smart asset control actioninstructs a parametric operational state for a smart asset or group ofsmart assets, the hierarchical smart asset control action instructs aparametric operational set point for a smart asset or group of smartassets, the hierarchical smart asset control action instructs aparametric operational constraint threshold for a smart asset or groupof smart assets, the accessing smart asset automation operationalprocess parameters associated with a hierarchical smart assetsystem/group is retrieved from a data store, and/or the operationalprocess parameters include historic or real time data.

In other embodiments the system may comprise, aggregating smart assetoperational data; and using smart asset operational data to improveefficiency or prospective process command selections.

Other embodiments of a method of Integrated Smart Asset Control Systemoptimization may comprise, accessing smart asset automation operationalprocess parameters; developing multi-faceted dynamic constraints at asmart asset level accounting for operational constraints associated withthe automation operational process; evaluating multi-faceted dynamicprocess constraint model for the smart asset to balance operationalprocess constraints and process objectives; determining an optimizedprocess operation point for the smart asset for the multi-faceteddynamic process constraints; transmitting data elements request by aparent used in part by the parent to determine a smart asset controlaction for the child smart asset; receiving a smart asset control actiongenerated from the optimized process operation point; and executing thehierarchical smart asset control action to transition from currentoperational values to operational values to achieve the balancedoperational constraints.

1. Hierarchical Asset Control Application Development

An industrial equipment operation/process is fundamentally a grouping ofequipment elements organized in a particular way working together toexecute a process. The ANSI/ISA-88 standard is one methodology fororganizing plant equipment In contrast to the process-centric modeldiscussed above, in accordance with the present disclosure, ahierarchical view of the operation/process can be developed as ahierarchical collection of assets, rather than a single control“process.” By modeling individual assets and groups of assets across ahierarchy, significant gains in efficiencies and efficacies may beachieved through increased granular control resolution. Such anasset-centric view of control facilitates development and execution of aunified control strategy for the industrial equipment operation/processwithout the drawbacks of the traditional process-centric controldevelopment approach.

The following description of the figures illustrates how underlyingequipment elements for an industrial system is used to develop aHierarchical Asset Control Application and corresponding controlhardware. Once the Hierarchical Asset Control Application andcorresponding control hardware are validated, they can be integratedwith the underlying equipment elements to create an Integrated SmartAsset Control System that effectively and efficiently executes anindustrial process through hierarchical control of smart asset and smartasset groupings. In some implementations, the Integrated Smart AssetControl System may be controlled and managed to optimize a variety ofoperational constraints and/or objectives.

FIG. 2A illustrates various aspects of the ANSI/ISA-88 standard fordescribing equipment and process, process control procedures andequipment 200. The general relationship between the process model 210,the procedural control model 220, and the physical model 230 isillustrated. A process 212 is a sequence of activities for action to betaken on material or energy. A process stage 214 is part of a processthat generally operates independently from other process stages. Processoperation 216 is a processing activity that usually results in a changein the material being processed. Minor processing activities that arecombined are a process action 218. A procedure 222 is the highest levelin the procedure control model 220 hierarchy and defines the strategyfor carrying out processing. Unit procedures 224 consists of an orderedset of operations. Operation 226 is an ordered set of phases thatdefines a processing sequence. The smallest element of proceduralcontrol is a phase 228. A process cell 232 is capable of orchestratingall processing activities. Units 234 coordinate the functions of thelower level entities. Equipment modules 236 coordinate the functions ofother equipment modules. Control modules 238 are the lowest level groupof equipment that can carry out control.

FIG. 2B is a diagram illustrating a typical industrial topology 250 ofan example industrial equipment operation/process in accordance with thepresent disclosure. Generally, the topology 250 is unique to anindustrial operation as it describes the underlying equipment assetsinvolved in the operation and how those assets interoperate to performtheir functions and produce business output. As depicted, the topology250 is hierarchical and includes primary assets at the lowest level.These primary assets combine into asset sets, which in turn combine intomore complex asset sets and so on. For example, primary assets cancombine into work cell asset sets (or process units) one level up. Thework cell asset sets in turn can combine into area/train asset sets onelevel up. The area/train asset sets further combine into higher levelsets called plant asset sets. The plant asset sets combine into fleetasset sets, which in turn combine into enterprise asset sets, andfinally into value chain asset sets.

FIG. 2C, illustrates an example mapping of the ANSI/ISA-88 a physicalequipment element hierarchy 275 with a packaging line hierarchy 280 isdepicted.

In accordance with the present disclosure, the Hierarchical AssetControl Application and corresponding control hardware are developed tofacilitate optimized granular control of smart assets (described below)in contrast to the typical industrial equipment topology 200. In otherwords, automation system architecture can be developed for eachindustrial operation in a way that ensures the architect architectureand topology of the automation system closely corresponds to or matcheswith the architecture and topology of the industrial operation. Thisalignment between the two architectures simplifies the tasks ofdesigning and maintaining automation systems by relevant users (e.g.,engineers, maintenance personnel and operators) as those users do notneed to understand and reconcile two differences in the underlyingequipment architectures 200 and the development of the HierarchicalAsset Control Application and corresponding control hardware that arelayered on top of and integrated with the typical industrial equipmenttopology 200. Diagrams of examples of smart asset Integrated Smart AssetControl System developed in accordance with the embodiments of thedisclosure are illustrated in FIGS. 5A and 5B and developed using smartasset building blocks illustrated in FIGS. 3A, 3B, and 3C and describedin greater detail below.

An industrial operation, in accordance with the present disclosure, canbe viewed as a collection of hierarchical smart assets, rather than asingle “process.” Such a hierarchical smart asset-centric viewfacilitates development and execution of a unified control strategy forthe industrial operation without the drawbacks of the traditionalprocess-centric approach. FIGS. 3A, 3B, and 3C illustrates aspects ofsmart assets that are the building blocks associated with developing aHierarchical Asset Control Application and corresponding controlhardware that facilitate granular controls and optimization, such asthose described with regard to the example of FIGS. 4A, 4B, and 4C.

One aspect of developing a Hierarchical Asset Control Application andcorresponding control hardware in accordance with the present disclosureinvolves utilizing a cyber-physical system (CPS) as a building-blockelement. A CPS is an autonomous or semi-autonomous control systemincluding sensors and/or actuators and an associated hardware (e.g., aprocessor or a computer) capable of executing software code/modules inthe form of intelligent agents (IAs) or “avatars” implementingmeasurement and control functions. FIG. 3A is a diagram illustratingexample components of a CPS in accordance with some embodiments of thepresent disclosure. As depicted, a CPS 300 can include one or moresensors 305, one or more actuators 310, a controller unit 320 and acommunication module 315 among other components. In some embodiments, asensor 305 and an actuator 310 can be embodied in a single device orunit. The actuators 310 can be embedded or networked into a smart asset.One or more intelligent agents 325 in the controller unit 320 canimplement a control strategy or algorithm to transform inputs such asmeasurement data from the one or more sensors 305 and any set pointsinto an output signal to improve the operational efficiency and/or othercharacteristics of the smart asset. The communication module 315 canfacilitate receiving of data from the one or more sensors 305 andsending of the output signal to the one or more actuators 310. In someembodiments, the communication module 315 can include a networkinterface that enables flow of data between the CPS 300 and other CPSsand/or higher level asset sets in the Integrated Smart Asset ControlSystem. The network interface can include one or more of a networkadaptor card, a wireless network interface card (e.g., SMS interface,Wi-Fi interface, interfaces for various generations of mobilecommunication standards including but not limited to 1G, 2G, 3G, 3.5G,4G, LTE, etc.), Bluetooth, a router, an access point, a wireless router,a switch, a multilayer switch, a protocol converter, a gateway, abridge, bridge router, a hub, a digital media receiver, and/or arepeater. In some embodiments, the communication module 315 can leveragethe Internet of Things (IoT) data to combine with other CPSs to formpart of an Integrated Smart Asset Control System that communicate withelements beyond the underlying industrial equipment, Hierarchical AssetControl Application and corresponding control hardware.

Extended control aspects of an Integrated Smart Asset Control System canbe grouped into two general categories of real-time control as (1)operational objectives and (2) operational constraints. For example,providing real-time control for the basic objectives of a business suchas profitability, operational profitability, and operational efficiencycan be one category of operational objectives. The second category ofoperational constraints can provide real-time control on the dynamicconstraints on the objectives such as safety risk, environmental risk,and security risk. FIG. 3B illustrates example components of an avataror an intelligent agent 325 in a CPS associated with a smart asset orsmart asset set for providing real-time control of the basic objectivesof a business and dynamic constraints of objectives (i.e., real-timecontrol over profitability, reliability, process efficiency, safetyrisk, environment risk, and security risk). In some embodiments, theintelligent agent 325 can include a RT profitability controller 330, areal time process efficiency controller 335, a real time reliabilityrisk controller 355, a real time environmental risk controller 345 and areal time safety risk controller 350. In other embodiments, theintelligent agent 325 may also include a real time security riskcontroller 360. It should be noted that in some embodiments, theintelligent agent 325 may include more or less controllers (or controlmodules), thereby providing control over more or less domains dependingon the particular implementation. In some embodiments, a coordinatormodule 340 may be included in the intelligent agent 325 for coordinatingthe execution of the controllers according to a control hierarchyassociated with an Integrated Smart Asset Control System such as the onedescribed in reference to FIG. 5B. In other embodiments, the intelligentagent 325 can include other modules besides the real time controllers.Examples of such modules can include, but are not limited to, ahistorization module 365 and a small (or big) data analytics engine 370.The real time controls facilitated by intelligent agent 325 makegranular smart asset or smart asset grouping control possible for anIntegrated Smart Asset Control System, it also facilitates IntegratedSmart Asset Control System operational optimization and systemmanagement.

One aspect of the Integrated Smart Asset Control System development isthe rapid implementation of avatars/intelligent agents corresponding tothe underlying for plant equipment elements. Plants are made up of alarge number of equipment elements to be monitored and controlled. Foravatars/intelligent agents to fully monitor/manage data associated withvarious aspects of the plant equipment elements, (such as security,safety, environmental, reliability, performance, profitability, dataand/or the like), a number of attributes associated with each smartasset can be considered and managed by an avatars/intelligent agent. Insome embodiments, avatars/intelligent agents are developed by usinggeneral templates developed based on vendor provided operationalspecifications, operational characteristics can be created as a libraryand provided for types of plant equipment assets that allow a user torapidly connect the equipment elements to the cyber avatars/instantiatedintelligent agents to develop a Hierarchical Asset Control Application.In some implementations, higher level intelligent asset templates canconnect the lower level smart assets together to form the higher levelsmart assets groupings to continue Hierarchical Asset ControlApplications development.

FIG. 3C is a diagram that illustrates example components of a smartasset 375 in accordance with some embodiments of the present disclosure.The smart asset 375 can include a CPS 300 described in reference to FIG.3A and other electrical components 385 (e.g., switches), a power system380 and the underlying equipment element 390 (e.g., the mechanicalhardware such as a pump, compressor, etc.). With the sensors, actuators,a controller including an intelligent agent, a communication module, thesmart asset 375 via the intelligent agent can autonomously orsemi-autonomously monitor and control its performance and operation. Insome embodiments, the smart asset 375 can utilize the communicationmodule including a network interface to connect to a communicationnetwork to communicate with other smart assets and smart asset sets,and/or report asset data, process data, asset health, alarms and events,and/or other data as appropriate for the particular application, to aremote computer or server system.

CPSs and intelligent agents enable design and development of aHierarchical Asset Control Application and corresponding controlhardware that can be integrated with the equipment elements of a typicalindustrial topology to create an Integrated Smart Asset Control System.With the decline in price, and power requirements and correspondingincrease in memory and compute capability, and networkability,CPSs/intelligent agents can be integrated with each piece equipmentelement of a typical industrial topology at a smart asset and/or smartasset grouping levels to create, efficiently and effectively manage,control, and in some instances optimize the Integrated Smart AssetControl System. By aligning CPSs/intelligent agents with the smartassets and smart asset groupings, the overall control problem of anindustrial operation can be partitioned into multiple smaller controlproblems for multiple autonomous or semi-autonomous control systemcomponents that can be networked together in flexible combinations. Thisbreakdown of the control problem into smaller manageable piecessignificantly reduces the complexity of the overall control problem andprovides various other advantages that are discussed throughout thisdisclosure. Such an overall control system using a baseline of equipmentelements from a typical industrial topology integrated with a hierarchyof smart assets and smart asset sets, each of which include anautonomous or semi-autonomous system comprising an intelligent agent toprovide real time control for the smart asset, smart asset set, oranother smart asset grouping is defined herein as an Integrated SmartAsset Control System (ISACS). FIGS. 5A and 5B illustrate examples ofIntegrated Smart Asset Control System hierarchical smart assetorganization and are described in greater detail below.

Extended Control Hierarchy

An extended control hierarchy such as the one depicted in FIG. 4A can bedeveloped for application of real-time controls in industrialoperations. Once smart assets are created and implemented as aHierarchical Asset Control Application in accordance with theembodiments of the disclosure described herein, smart management andoptimization is significantly improved and granular operationalobjective and operational constraint control and optimization may berealized. FIG. 4A illustrates a spectrum of operationalconstraint/operational objective variables 400 that can becontrolled/optimized for smart assets/groups of smart assets 375. It isto be understood that depending on the operational characteristicsassociated with a particular implementation, other prioritized spectrumsare also possible. It is also possible to have the operationalconstraints and objectives variables 400 equally distributed for eachsmart asset-every smart asset has the ability to manage all theoperational constraints and objective variables 400 as appropriate. Asillustrated in FIG. 4A, an embodiment of prioritized operationalconstraints/objectives can be managed across each of the levels in thehierarchy or distributed and split across different levels of thehierarchy. For example, distributed constraint/objective managementillustrated in FIG. 4A, involves security risk, being a prerequisite,and as such is controlled, managed and optimized at the lowest level inthe hierarchy. Safety risk and environmental risk are located at thenext lowest levels of the hierarchy, respectively, due to the criticalnature of these two risks. Above safety risk and environment risk isasset reliability risk, since a reduction in reliability can result inreduced asset performance or even asset failure which would severelylimit the value from the asset. Above reliability can be traditionalprocess control for efficiency. Finally, where contextually appropriate,is real-time profitability control. Bringing each component ofindustrial operations into safety risk, environmental risk, reliabilityrisk, efficiency and profitability control can help drive maximum valuefrom each smart asset and form one way to optimize the HierarchicalAsset Control Application and corresponding control hardware. TheHierarchical Asset Control Application and corresponding controlhardware, in some embodiments, applies the extended real-time controlsto each equipment asset, unit/work cell, area/train, plant, fleet,enterprise and/or value chain. Accordingly, each smart asset and smartasset set or other smart asset grouping can operate in a safe,financially, and environmentally optimal manner. In this way, the entireenterprise and value chains can be brought under real time control andfacilitates optimization of control and management in real time forseveral operational constraints/operational objectives.

2. Extended Real-Time Control

The Hierarchical Asset Control Application (HACA) as described above notonly addresses the challenges associated with controlling theoperational efficiency of industrial operations, but also facilitatesimplementation of a unified control strategy. Beyond operationalefficiency, other new domains are in need of effective real-timecontrol. In some embodiments, the Hierarchical Asset Control Applicationcan be extended to effectively control these new domains such as, butnot limited to: reliability risk, safety risk, environmental risk, andprofitability.

It should be appreciated while various types and numbers of constraints,such as safety risk, and various types and numbers of objectives, suchas real time operating profit are described herein, there are nolimitations as to the type and number of constraints or objectivescontemplated herein.

Traditionally, most of the variables associated with the measurement andmanagement of the profitability of industrial operations changedinfrequently. For example, in the past, industrial plant managementcould typically develop contracts with their electricity suppliers whicheffectively set the price per unit of electricity for up to a year. Withthe price constant for a year at a time there was no need to try tocontrol it. Today, with the deregulation of electric power gridsthroughout the world, the price of electricity can and often does changeon a much more frequent basis. For example, in the open power grid inthe United States, the price can change every 15 minutes. In the UnitedKingdom. the price can change every 20 minutes. So, this businessvariable (i.e., price of electricity or energy cost) that had been moreor less constant in the past is now experiencing real-time variation.This is also true in many regions of the world for natural gas, rawmaterials and production value (the value of products produced in anindustrial operation). The traditional approach to trying to managethese business variables with monthly data generated by EnterpriseResource Planning (ERP) reports is no longer sufficient. TheHierarchical Asset Control Application can facilitate this transition inindustry from being able to manage business variables in a transactionalmanner to real-time control by enabling extended real-time control insmart assets and smart asset sets or other smart asset groupings.

In some embodiments, some business variables of industrial operationsexperiencing real-time variability include, but are not limited to:energy cost (the cost of energy per consumed unit), material cost (thecost of raw material per consumed unit) and production value (the valueper unit of products produced). Since these three composite variablestend to fight each other they can be controlled together to maximizeoperational profitability as the waterfall diagram in FIG. 4B depicts.All four of the components in this waterfall diagram experience a degreeof real-time variability and as such are candidates for effectivereal-time controls as provided by the Hierarchical Asset ControlApplication.

The components of the waterfall diagram depicted in FIG. 4B represent aconstrained, multi-objective optimization problem as shown in asimplified diagram of FIG. 4C. Although this model is a grosssimplification of the actual optimization challenge, it serves todemonstrate some of the characteristics of the extended control domain.For example, the three components of real-time profitability aretypically constrained by a composite of safety risk, environmental riskand equipment limits in terms of maximum operational throughput andreliability. Since maximum operational throughput is generally a fixedvalue that may not be controlled when the operation is in progress, theconstraints that may generally be controlled are safety risk,operational risk and the reliability of the equipment, referred to asthe reliability risk. The combination of these models shows howreal-time process control can be extended beyond just the traditionalapproach to control for operational efficiency improvement to includereal-time control of safety risk, environmental risk, reliability andprofitability within a Hierarchical Asset Control Application.

The application of real-time control to an objective, such as improveoperational profitability, is depicted in FIG. 4D. A prerequisite forcontrol of such an objective, is the ability to measure the variables tobe controlled. In this case, it involves measuring operationalprofitability through real-time accounting (RTA). RTA is described indetail in U.S. Pat. No. 7,685,029 titled “Method for Real-TimeActivity-Based Accounting,” which is incorporated herein by reference inits entirety. Once the measurements are made, the information and RTAscan be gathered and displayed in real-time decision support dashboardsfor consideration by personnel responsible for the subject section ofthe operation. Using this information, the personnel can supervise theautomatic control of profitability in some embodiments. In otherembodiments, direct manually control of profitability in a mannersimilar to manual process control is also possible. In other embodimentsa combination of automatic control and manual operation is alsopossible. In the case of the automatic control approach, an automaticprofit controller can be implemented. The automatic control approach maybe preferable over the manual control approach in instances where speed,complexity of analyses and repeatability are important considerations.In this manner, the real-time profitability of the operation can becontrolled.

As noted above, real-time profitability is constrained by thereliability risk, safety risk, and environmental risk constraints of thesection of the operation under consideration. The profitability of theoperation may be improved by effectively controlling these risks. Tocontrol these risks, an autonomous or semi-autonomous control system canmeasure each of these constraint variables in real-time, empowering theappropriate personnel with the real-time measurement information. Insome embodiments, the real-time measurement information along with anyother inputs can be used by the autonomous or semi-autonomous controlsystem to control each variable in turn. Applying real-time control tothese constraint variables may result in a lifting of the constraintsenabling more operational profitability in a safe and environmentallysustainable manner, which are important for most production andmanufacturing operations.

Real-Time Control Model

As used herein, real-time control incorporates decisions based onmeasurements to impact a desired outcome in a timeframe associated withthe time constant of an operational or business process. Real-timecontrol can include automatic and manual control executed either onfeedback or predictive measurements as depicted in FIG. 4E.

Applying extended control by the Hierarchical Asset Control Applicationincludes utilizing real-time measurements available within theHierarchical Asset Control Application to apply real-time controls toimprove security rules, safety risk, environmental risk, reliabilityrisk, efficiency (traditional process control) and profitability in realtime. As with traditional process control which is applied to increaseoperational efficiency, the actual variables under direct control may belower level variables than those of the objective for the control. Forexample, for the objective of improving operational efficiency, theactual direct control can be performed on variables or operationparameters such as flow, level, temperature and pressure of the assets.Improving the control of these variables to values that improveoperational efficiency meets the objective in some embodiments. The sameis true for objectives such as security risk, safety risk, environmentalrisk, reliability risk and profitability objective.

The appropriate mechanisms for control of a Hierarchical Asset ControlApplication can be categorized along two dimensions as depicted in FIG.4E. The control can either be manual or automatic, and it can either bebased on feedback, predictive and/or any suitable techniques. All fourcategories in the diagram represent valid options for applying controlsto a Hierarchical Asset Control Application. One example progression canbe from manual feedback control, to automatic feedback control, tomanual predictive control, to automatic predictive control. This ordercan vary in different implementations. The cost of feedback control istypically considerably less than the cost of predictive control and,therefore, unless significant incremental value can be realized byapplying predictive control most industrial operations may utilizefeedback approaches.

Manual control is typically the most appropriate approach when engineersare trying to learn the dynamics of a system in order to bettercharacterize or model the system to develop and apply automatic control.Since many of the objective variables being brought under controlthrough the extended control characteristic of asset control systemshave not been measured or characterized in real-time previously, manualcontrol approaches may be utilized until the characteristics of theobjective variables is understood to the point at which automaticcontrol can be effectively developed.

Automatic controls have been implemented in two basic ways in industrialoperations. First is logic control that was developed for machinecontrol applications but has been utilized in many other applications.Second is algorithmic process control that was developed for morecontinuous process applications. Combinations of these two traditionalcontrol approaches can be utilized to provide automatic control ofprofitability objectives, reliability risk, safety risk, environmentalrisk, and/or security rules from across the entire ISCAS from the smartasset and smart asset set or other smart asset groupings all the way tothe enterprise level in some embodiments of the present disclosure.

FIG. 5A illustrates a Hierarchical Asset Control Application andcorresponding control hardware as an Integrated Smart Asset ControlSystem 500 for an example industrial equipment operation/process inaccordance with a first embodiment of the present disclosure. Anindustrial equipment operation/process can comprise primary (or base)smart assets 501 with integrated functions at the lowest level. In thecase of an oil refinery for instance, the primary smart assets 501 canbe equipment such as pumps to force crude oil to move in a specificdirection and heat exchangers to heat incoming crude oil. Each of theseprimary smart assets 501 can be autonomously or semi-autonomouslycontrolled by a Cyber Physical System (CPS) having an intelligent agentto ensure optimal or effective operation as described above or linked asa grouping of smart assets also controlled as an element within aHierarchical Asset Control Application and corresponding controlhardware.

As depicted, in some embodiments, each CPS in the Integrated Smart AssetControl System is aligned with a smart asset, and has its own autonomousor semi-autonomous control system including an avatar or intelligentagent (IA) that defines and executes a control strategy specific to thesmart asset and in some instance to the smart asset as part of theHierarchical Asset Control Applications and corresponding controlhardware. For example, the Hierarchical Asset Control Applications andcorresponding control hardware and underlying equipment elements workingtogether to form an Integrated Smart Asset Control System 500illustrated in FIG. 5A, each of the low level smart assets (e.g.,primary assets) can have a CPS including an intelligent agent that isresponsible for the autonomous or semi-autonomous control of the smartasset. Similarly, (as show in FIG. 5B) higher level smart assets (e.g.,smart asset sets) can have their own intelligent agents that effectivelyincorporate the lower level smart assets and/or smart asset sets and/orcoordinate the activities of intelligent agents at the lower levels. Forexample, the intelligent agents controlling the overall unit/work cellcan be incorporated into intelligent agents controlling trains/areas.The train/area intelligent agents can be incorporated into plantintelligent agents, which can be incorporated into fleet intelligentagents, which in turn can be incorporated into enterprise intelligentagents and finally into value chain intelligent agents. In other words,in the Hierarchical Asset Control Applications and corresponding controlhardware and corresponding integrated smart asset control system, thecontrol strategy at the higher level subsumes the control strategies atthe lower levels facilitated by intelligent agent characteristics suchas polymorphism, inheritance, late binding, encapsulation, and/or thelike.

In some embodiments, the control system associated with each CPScontrols the functions within the associated asset and the transfer ofany information required from other CPSs or required to be sent to otherCPSs. Since the control system within each CPS provides appropriatecontrol for the sensor based data associated with the smart asset, mostof the base data generated within each CPS is used inside the controlstrategy for the CPS and does not need to be accessed anywhere else inthe overall plant system. Moreover, effective autonomous orsemi-autonomous control of the primary assets by the intelligent agentsprior to their incorporation into the hierarchical smart asset setcontrol strategy means that the smart asset set level or other smartasset grouping control becomes much simpler to design and build.

The smart asset control system thus brings entire enterprises and valuechains into real-time control to provide improvements in unified andcoordinated operation of industrial businesses to drive significantbottom-line improvements. Moreover, with this architecture, the controlcan be expanded to the enterprise control system level and even to thevalue chain control system level. This asset-centric perspective ofindustrial operations and enterprises not only makes the controlstrategies associated with them simpler to design, implement andexecute, it actually represents the way in which industrial companiesperceive their basic operations. For example, engineers in industrialoperations typically understand and describe the operation from an assetperspective. This natural alignment with the way industrial personnelunderstand their operations and jobs improves their understanding of theautomation system because the system is aligning with their perspective.

It should be noted that other than the lowest (primary smart asset)level of the architecture depicted in FIG. 5A or 5B which is coupled toa CPS, the topology of the Integrated Smart Asset Control System can bedefined by software and developed as a Hierarchical Asset ControlApplication and corresponding control hardware. As such, the resultingIntegrated Smart Asset Control System in accordance with a firstembodiment of the present disclosure has a software-defined architectureas a Hierarchical Asset Control Application.

FIG. 5B illustrates an Integrated Smart Asset Control System for anexample in accordance with a second embodiment of the presentdisclosure. In this embodiment, higher level asset sets can also have anassociated CPS having an intelligent agent 550. For example, as depictedin FIG. 5B, each primary smart asset 551, unit/work cell 552, area/train553, plant 554, fleet, enterprise and value chain smart asset sets canhave an associated CPS including an intelligent agent. As such, thetopology of the Integrated Smart Asset Control System as depicted inFIG. 5B can be defined by a combination of hardware and software as aHierarchical Asset Control Application and corresponding controlhardware. In some embodiments, a CPS associated with a smart asset setcan have a different set of components from a CPS associated with asmart asset. For example, types of sensors and actuators in a CPSassociated with a primary smart asset can be different from those in aCPS associated with a smart asset set. Components such as specific typesof sensors and actuators can be selected based on the environment inwhich a smart asset or smart asset set operates. FIGS. 7-8E discuss ingreater detail how a Hierarchical Asset Control Applications andcorresponding control hardware can be developed.

FIG. 6 illustrates another example of underlying industrial equipmentoperation/process, as a foundation upon which a Hierarchical AssetControl Application and corresponding control hardware may be applied tophysical equipment hierarchy in accordance with some embodiments of thepresent disclosure.

FIG. 6 depicts a distillation column 600 (e.g., in an oil refinery).Once configured as smart assets/smart asset groupings incorporating CPSand intelligent agent components, the distillation column can beconsidered a unit asset set within a Hierarchical Asset ControlApplications. In the example, the equipment/can be broken down intothree basic sections: feed, bottoms, distillate. The bottoms section ismade up of a collection of equipment including pumps, valves,measurement devices, etc. The smart asset control system is developedand provides each of the three equipment modules with a Cyber PhysicalSystem (“CPS”) including an avatar/intelligent agent that areresponsible for providing monitoring and control functions for theunderlying equipment. The Hierarchical Asset Control Application andsystem control hardware also provides the distillation column smartasset set with an intelligent agent that can coordinate the lower levelintelligent agents associated with and controlling the three equipmentmodules. In some embodiments, the Integrated Smart Asset Control Systemor smart asset control system can provide a CPS to the distillationcolumn smart asset set as well as the three equipment modules.

As will be discussed in greater detail, in FIGS. 7-8E, the developmentof a Hierarchical Asset Control Applications and corresponding controlhardware involves accessing a library of intelligent asset templatesthat are instantiated as instances of the smart asset avatar/intelligentagent. For example, the distillation column described in FIG. 6 maycorrespond with an intelligent asset template (described in FIG. 8C ingreater detail). In some implementations the equipment elements of FIG.6 may also be used to define and identify corresponding intelligentasset templates to develop a Hierarchical Asset Control Application forthe components of the distillation column as well as the distillationcolumn itself. The intelligent asset templates can include informationand control actions related to security, safety, environment,reliability, performance, profitability, and the like (an exampletemplate is shown in FIG. 8C and described in greater detail below).Operators may interact with the Hierarchical Asset Control System at lowlevels and/or at higher levels. The avatar templates/libraries allowhigh level operator adjustments to automatically be sent as commands tothe lower level plant assets facilitated through assetcontrol/efficiency elements of a template and the developed HierarchicalAsset Control Applications.

Hierarchical Asset Control Application Development

FIG. 7 is a flow diagram illustrating a method of Hierarchical AssetControl Application and corresponding control hardware development andintegration to create an Integrated Smart Asset Control System inaccordance with various embodiments of the present disclosure. It is tobe understood that depending on the implementation and application thesystem facilitates and various aspects of the feature and functionallydescribed herein may be implemented across any number of computingdevices implementation some of the components illustrated in FIG. 27.For example, Hierarchical Asset Control Applications development,control hardware identification, aspects of Integrated Smart ControlAsset Systems integration, run time optimizations, and/or systemmanagement and/or control may be implemented as desktop application,mobile applications, cloud computing based applications and/or any othernumber of computer elements or implementations. Elements of the overallflow for developing a Hierarchical Asset Control Application, aredescribed in more detail in FIGS. 8A-8E. FIGS. 9-12 illustrate aspectsof Hierarchical Asset Control Application and corresponding controlhardware development and integration to create an Integrated Smart AssetControl System according to an example involving Exothermic Reactorequipment elements.

As illustrated in FIG. 7, Hierarchical Asset Control Applicationdevelopment process 700 involves first determining a list industrialequipment elements that the will be controlled and in some instances thecontext in which the equipment elements will be controlled 710 tofacilitate an operational industrial application. From the equipmentelement list, a first equipment element is selected and the systemqueries an intelligent asset template library 725 to select on or moreintelligent asset templates 720. The system instantiates the intelligentagent template for a smart asset (or a group of smart assets) 730. Onceinstantiated, the intelligent agent data including operationalcharacteristics, objective and constraint data is commissioned anddeveloped from the appropriate data sources or database(s) 740. Oneelement of the intelligent agent data involves smart asset parent/childcontrol information, as well as smart asset interconnectivityinformation. This information is used to develop the smart asset parentchild control relationships as the Hierarchical Asset ControlApplication is developed 750. The Hierarchical Asset Control Applicationis validated 760 and if approved used to develop correspondingimplementation control hardware requirements. An Integrated Smart AssetControl System can be developed as the Hierarchical Asset ControlApplication; any corresponding control hardware elements are integratedwith the underlying industrial equipment elements 770. Finally, thecorresponding control hardware and equipment elements are integrated toform the Integrated Smart Asset Control System to develop a turnkeyhierarchical industrial process control system 780 (e.g., as illustratedin FIG. 5B).

FIG. 8A is a flow diagram illustrating aspects of determining andverifying equipment elements in greater detail in accordance withvarious embodiments of the present disclosure. This logic flowrepresents various processes which correspond to enumerating equipmentelements and characteristic parameters which describe that equipment710. An equipment list is obtained 800 which may correspond to acomplete or a portion of an industrial system to be transformed into avirtual industrial system. Obtaining this equipment list may be manualor automatic in nature and need not be a listing of equipment for acomplete system. Once obtained, an equipment element on the list isselected 802. Depending on the particular implementation, there may beseveral methods by which to select this first equipment element. It maybe selected based on criticality, size, known associated childrenassets, or any other characteristic or any combination. Any equipmentmay be chosen, so long as it part of the system to be analyzed. As theequipment element is analyzed for its own characteristics, any equipmentdetermined as a “child” to this equipment is also captured in thecharacterization 804. It should be appreciated that one “parent”equipment may have several “child” equipment relationships andprincipals of the disclose provide for the converse. The system willquery of each equipment element has been identified 806 and if not eachequipment element of the entire list will be identified 808 andanalyzed. A verification that each equipment element has been processed810 will be performed after the last equipment element on the equipmentlist has been processed.

Determination of equipment may be created by automated or user directedmethods or accessed in an equipment library, or some combination ofboth. When entered, the system may query each logical location for aspecified piece of equipment and receive information describing variouscharacteristics of the device. Automated methods may include “crawler”functionality where given a series of logical addresses, the systemitself may query each logical location for a specified equipment elementand receive information describing various characteristics of thatequipment element.

Once a complete equipment list has been determined, an equipment mappingis processed where each piece of physical equipment is mapped with oneor more intelligent asset templates 720. An example of an intelligentasset template and corresponding data elements 840 is illustrated inFIG. 8C. To assist in the mapping of each piece of physical equipment,an intelligent asset template library 725 is accessed for each equipmentelement. If it is determined that one or more characteristics of thephysical equipment match that of the virtual intelligent asset template,a mapping is formed.

It should be appreciated this mapping of physical equipment tointelligent asset template need not be a one to one relationship. A manyto one relationship may exist determined in part by the relativecomplexity of the characteristics associated with the physicalequipment. It should be appreciated that an intelligent agent may becreated for each smart asset, a group of smarts assets organized as asmart asset set, a group of smart asset sets organized as a smart assetunit or, a smart asset may be composed of one or more equipmentelements. Further, principles of this disclosure contemplate that morethan one intelligent asset template may be used to describe a singleequipment elements or more than one intelligent agent may be used todescribe a single smart asset or single set of smart assets, or othergrouping of smart assets.

As the mapping is completed for each piece of physical equipment, anintelligent asset data structure is created for each equipment element730. There are several possible relationships between equipment elementsand intelligent agent templates. They include a one to one relationshipbetween the equipment element and intelligent asset template, as well asmany to one relationship between the equipment element and intelligentasset template. It should be appreciated; each piece of equipment istransformed into a virtual smart asset which represents each instance ofthe physical equipment.

To develop an accurate virtual model of the equipment elements forincorporation into a Hierarchical Asset Control Application, severalcharacteristics are developed and populated into an intelligent agentdata structure for each equipment element 740. Data amounts and typescontained in the intelligent agent data structure may include assetidentification data, asset industrial application data, physicalmodeling data including, but not limited to availability, platform, andsecurity constraints, as well as hardware and system characteristics todescribe the equipment element in detail.

This process continues for all equipment element within the industrialapplication until proposed hierarchical arrangement of parent to childasset control relationships are developed 750. This mapping furtherdevelops the virtual intelligent agent data structures of each equipmentelement to form a complete virtual hierarchy of characterized assetsrepresentative of the physical equipment element associated with theindustrial system.

This complete virtual hierarchy of characterized assets may be eithervalidated 760 and/or simulated to confirm the virtual representation ofthe Hierarchical Asset Control Application operates in an expectedmanner. This complete Hierarchical Asset Control Application may then beprovided to a system for operation of the corresponding physicalindustrial system.

FIG. 8B is a flow diagram illustrating determining an intelligent assettemplate and identifying an intelligent data template in accordance withvarious embodiments of the present disclosure. This logic flowrepresents various processes which correspond to the selection of one ormore intelligent asset templates 720, creation of intelligent agentinstantiation 730, and development and population of an intelligentagent 740 illustrated in FIG. 7. For each verified equipment elementwhich has been processed, a corresponding intelligent agent instance isdefined 820 based on the equipment list and the available entries in theintelligent asset library 822. Once an intelligent agent instance isdefined for a given smart asset 820, smart asset instances are definedfor any known children smart assets 824 of the parent asset just defined820.

It should be appreciated that there may be intelligent asset templateswithin the intelligent asset library 822 that are not complete in partor not represented at all. This disclosure contemplates thesepossibilities and allows for subsequent smart asset characterization toupdate the intelligent asset library 822. In this way, the intelligentasset library 822 will improve over time with each subsequent systemuse. This disclosure also contemplates the use of “generic” intelligentasset templates which may be available from organizations such as ASMEor IEEE. Such intelligent asset templates may lack some informationbeneficial to the process described herein, however may be useful indescribing some basic characteristics of the equipment elements (e.g. achiller pump, etc.).

For each smart asset instance if it is determined that one or morecharacteristics of the physical equipment matches that of theintelligent asset template a mapping is formed. It should be appreciatedthis mapping of smart asset to intelligent asset template need not be aone to one relationship, a many to one relationship may exist determinedin part by the relative complexity of the characteristics associatedwith the equipment element. As the mapping is completed for each smartasset, an intelligent agent data structure is created for each smartasset 826. There is a one to one relationship between an equipmentelement and a smart asset. Each smart asset may serve as the virtualrepresentation of the equipment element within the construct of thedeveloped Hierarchical Asset Control Application.

To develop an accurate virtual model of the equipment element, severalcharacteristics may be developed and populated into the intelligentagent data structure for each physical asset. To assist in determiningthe operational capabilities of a particular smart asset, for aparticular industrial application, characteristics such as constraints,are defined for each smart asset 828. These constraints may include, butare not limited to, environmental, reliability, and safety constraintsdefined on a per asset basis. It should be appreciated that while theremay be common equipment elements, how that equipment element is used fora particular industrial application may substantially affect the natureof one or more operational constraint(s), as the operationalcharacteristics a smart asset. As one of a variety of examples, a pumpwhich is required to operate continuously to move drinking water fromone location to another, may have a substantially shorter service lifethan the same pump only operating twice a day to move marshmallow flufffrom one vessel to another due to the batch nature of the operation. Insuch a case, reliability, safety, and environment factors for theidentical equipment elements (e.g. the pump) may vary substantiallyacross any number of industrial applications. Each of these operationalconstraints are defined 828 for the smart asset and incorporated intothe intelligent agent on a per smart asset basis (or in some instancesfor a smart asset grouping) 830. Each virtual asset representation maypossess various characterizing data that will in part determine how aparticular industrial application will interact with the supportingequipment. It should be appreciated that an array of constraint data maybe incorporated into each asset. In various embodiments ofsmart/intelligent assets, sets, subsets and in some instances theconstraint may have a null value or value to be determined after theIntegrated Smart Asset Control System is operational. Althoughoperational constraints such as safety, reliability, environmental andcybersecurity constraints are discussed herein, it should be appreciateda wide array of constraint types and underlying data may be implemented.

Parent/child relationships control relationships are created 831 todevelop the hierarchy of smart assets and aspects of the correspondingcontrols that are possible for the Integrated Smart Asset ControlSystem. Once all characteristics are defined for the asset, includingany defined constrain data 828, the intelligent asset data structure ispopulated for the asset 832 and stored within an intelligent assetdatabase 834 for reuse as needed. Examples of this reuse may besubsequent commissioning of an asset or series of assets for a similarindustrial application. Further, it should be appreciated with repeateduse the precision of the intelligent asset data structures tocharacterize assets in various industrial applications may increase,thereby furthering one of the stated goals of industrial design, to makea system well characterized and repeatable. This process is repeated forall smart assets within the industrial system 836. Once each smart assetis defined after instantiation/commissioning as an intelligent agent,the complete system smart asset list 838 will be available for furtherprocessing.

FIG. 8C is an illustration of an intelligent asset template and examplesof the data structure 840 which may be contained in accordance withvarious embodiments of the present disclosure. To develop an accuratevirtual model of the physical asset, several characteristics aredeveloped and populated into the intelligent agent data structure foreach equipment element 740. Data amounts and types contained in theintelligent agent data structure are various but may include assetidentification data, asset industrial application data, physicalmodeling data including, but not limited to availability, platform, andsecurity constraints, as well as hardware and system characteristics todescribe the physical asset in detail. It should be appreciated thetypes and quantity of data is various based on the specific industrialapplication and corresponding supporting equipment.

FIG. 8D is a flow diagram illustrating validating and simulating aHierarchical Asset Control Application in accordance with variousembodiments of the present disclosure. Depending on the particularimplementation of the system, either of these steps may be omitted ormade optional. As the complete system smart asset list is available 842and processed from equipment element to intelligent asset datastructure, optional validation and/or optional simulation may beperformed before a proposed solution is placed into service with aphysical industrial system. With the parent/child control relationshipsmapped for each smart asset, validation occurs to verify that eachcommunication and control pathway exists and is usable 844. If it isdetermined that issues related to these parent/child communicationpathways exist 846, remediation is undertaken 848, and continues untilresolved 852. Remediation may include, but is not limited to, alteringthe equipment list, smart asset list, and/or the intelligent agent datastructure. It may be beneficial this validation occur before actualhardware control in an industrial application occurs to prevent thepossibility of very serious results. Once the asset parent/childrelationship is validated, a Hierarchical Asset Control Applicationexists.

However, before application or instantiation to a hardware based system,simulation of the complete, validated, Hierarchical Asset ControlApplication 850 while optional 862 may be beneficial, to determine ifany condition exists or may arise that would exhibit unexpected orundesired results in a hardware based industrial system. As withvalidation 844, the entire Hierarchical Asset Control Application issimulated 850. If it is determined that any issues related to simulatedoperation of the Hierarchical Asset Control Application exist 854,remediation may be undertaken 856, and may continue until resolved 858.

When complete, a validated and simulated Hierarchical Asset ControlApplication exists and may be utilized 860 to develop correspondingcontrol hardware requirements. The Hierarchical Asset ControlApplication corresponding control hardware can then be used to integratethe control hardware/software with the underlying equipment elements toultimately create an Integrated Smart Asset Control System (e.g., asillustrated in FIG. 5B). In some implementations, the Integrated SmartAsset Control System or the Hierarchical Asset Control Applicationcorresponding control hardware may be vetted using a simulation toolbefore actual hardware integration and/or runtime execution as anoperational industrial application occurs.

FIG. 8E is a flow diagram illustrating hardware instantiation of aHierarchical Asset Control Application in accordance with variousembodiments of the present disclosure. Once a validated and simulatedHierarchical Asset Control Application exists and is retrieved by thesystem 870, a request may be made automatically from the system itselfor input from a user to request spare capacity of the proscribed hostresources 872. Once complete, a list of required hardware platforms maybe generated 874 and platform requirements for all smart assets storedwithin the intelligent agent database. A hardware platform type of isselected 875 and assignment of the intelligent agents requiring ahardware platform to a target machine continues 876. Each intelligentagent may include starting a list with one target machine instance forevery required hardware platform type, selecting one hardware platformtype, selecting an intelligent agent for that hardware platform type,collecting availability and resource requirements for the intelligentagent industrial application, identifying the number of targets requiredfor the application, adding other target hardware platform as needed,attempting to install the application to the required number of targetmachines, checking the characteristics of the application againstremaining available capacity of the target hardware platform.

Embodiments of this disclosure contemplate the balancing of availableresources which may involve considering if more than one hardware typeis in use and if the application fills the spare capacity requirement todetermine an appropriate alternate resource. If a resource is overcapacity the system may remove resources from target, otherwise generatea new target machine(s) and subtract resources from previouslydesignated target machine(s). This process may continue until allintelligent agents are processed within a particular platform 880 andfor all agents 882. Once all intelligent assets are processed 882 andthe various characteristics of deploying a hardware instantiation of thevalidated hierarchical industrial asset system/application have beendetermined, a summary report is generated 886 that compares the optionsto the system designer and defines a preferred configuration. At thispoint the system or a user of the system may select and adjust variousparametric options 886 to adjust the existing configuration of theproscribed host resources defined 874. These adjustments may affect theoperation of the industrial system and any may be adjusted to operatethe system in an alternate manner.

One example of such a condition would be to adjust the system to adesired optimal state to maximize on a particular characteristic, suchas profit, runtime, or safety. Many such adjustments are possible andcontemplated in various embodiments within this disclosure. Once thevarious adjustments are made, if desired, the finalized hardware designis submitted to a repository such as the system definition database 888for use at a later point in time.

It should be appreciated the system described in FIG. 8E is able to beinstantiated using several configurations of computational resources.Examples may include resources described in FIG. 28. Principles of thedisclosure contemplate the distribution of any computational resourcesto achieve the benefit of distributed failure points, reduced cost ofinfrastructure, modularity, portability, or other benefit specific tothe particular industrial application.

3. Example Processing

FIG. 9A is a diagram illustrating industrial process cell equipmentorganization examples in accordance with various embodiments of thepresent disclosure. An example cell that contains three reactor units900 is illustrated. Depending on the implementation of the system, theHierarchical Asset Control Application can be developed for a singleunit and replicated as appropriate for a particular cell and runindependently and/or in coordination. Reactor Unit A 904, Reactor Unit B905, and Reactor Unit C 906 are connected to a chilled water reservoir901 that is shared for all reactor units supplied by a pump 902 withinthe process cell 900. A temperature indicator 903TI and flow indicator903FI provide a measurement of the temperature and flow of effluentleaving the plant. Valve positioner 903FV controls the flow of coolingwater that goes directly into the effluent to reduce the temperature ofthe effluent.

It should be appreciated that FIG. 9A is one representation of theequipment that many be controlled by an Integrated Smart Asset ControlSystem, which may be at a Unit/Work Cell Asset level and FIG. 9B may beat a smart asset level as described in FIG. 5B. The complete rector unit905 of FIG. 9B, may correspond to Reactor Unit A 904 in FIG. 9A. One orboth may be Integrated Smart Asset Control System structures dependingon the particular industrial application. Development of theHierarchical Asset Control Application is contemplated for the varioushierarchical levels of the entire structure.

FIG. 9B is a diagram illustrating an industrial process examples inaccordance with various embodiments of the present disclosure.

A complete reactor unit 905 is illustrated which includes a reactorvessel 910 and a number of sensors and valve positioners. Pressureindicator 910PI, temperature indicator 910TI, and level indicator 910L1provide measurements inside the reactor vessel. A reactor cooling jacket911 with associated flow valve 911FV controls the flow rate of coolingwater into the reactor cooling jacket 911. A valve positioner 915FVcontrols the flow of warm water from the reactor cooling jacket 911 tothe effluent of the plant and is monitored by a flow indicator sensor915F1 and temperature indicator 915T1 to provide measurement of thetemperature and flow of the effluent leaving the reactor unit 910.

The reactor agitator 912 stirs the reagents to ensure complete reaction.“Product C” has an associated pump 913 and flow valve 913FV. Anemergency reaction quench tank 914 and associated flow valve 914FV isavailable to stop the reaction and will solidify reactants and renderthe reactor vessel 910 unusable.

A heat exchanger 920 that controls the temperature of the cooling waterpumped to the reactor jacket 911 is associated with a series of sensorsand valve positioners. Temperature indicator 920T1, flow indicator920F1, and valve positioner 921FV measure and maintain the chilled waterto the heat exchanger 920. The fresh water pump 940 pumps fresh waterinto the heat exchanger. This fresh water is what is pumped into thereactor jacket 911 to remove heat from the reactor vessel 910. A valvepositioner associated with this fresh water pump 940FV controls the flowrate of the cooling water entering the heat exchanger 920.

“Reagent A” pump 950 pumps the reagent material into the reactor vessel910. Related to the pump are a flow sensor 950F1 and a valve positioner950FV. A similar structure exists for “Reagent B” where a pump 960 pumpsthe reagent into the reactor vessel 910. Related to the pump are a flowsensor 960F1 and a valve positioner 960FV.

A pressure relief valve 970 vents open when the pressure inside thereactor vessel 910 exceeds a limit. This vents to a smoke stack which isopen to the outside environment.

In the present example a chiller and reservoir 980 supply is used tocool the public water to a desired temperature and pump 985 into thereactor cooling jacket 911. Further, in abnormally high temperatureconditions, it may be pumped directly into the reactor cooling jacket911 to quickly slow or stop a reaction while preserving the ReactorVessel. This chill water may also be added directly to the effluent toreduce the BTUs being released to the environment.

FIG. 10 is a diagram illustrating aspects of an example of initialprocessing of underlying equipment elements to start development of theHierarchical Asset Control Application in accordance with variousembodiments of the present disclosure. In the illustrated exothermicreactor industrial system 1000, an equipment list is determined andobtained 1040 to describe the equipment elements and their respectiveparameters that comprises the reactor industrial system 1000. This listwill be composed of the various equipment elements illustrated anddescribed in FIG. 9. A first equipment element is selected 1050 to beginthe processing of the equipment list. In the present example, theexothermic reactor 910 is chosen. As noted previously, any equipmentelement may be chosen to begin the process. The exothermic reactor 910was chosen in this case as it is a fundamental piece of equipment in thesystem and is known to have various parent/child relationships withasset groupings such as the flow valve 913FV and any associatedequipment for “Product C” 1010, a heat exchanger asset grouping 1020,the flow valve 960FV associated with the “Reagent B” asset grouping1030. Each child of the exothermic reactor 910 is queued for processingand noted as a potential parent/child control element for subsequentprocessing. For example, added to a list of potential child controlequipment element connected to the parent asset—exothermic reactor 910.Each equipment element is processed in the list until all equipmentelements have been processed 1060. In the present example, the heatexchanger 920 may be processed and all children to the heat exchanger920 identified such as fresh water pump 940, and flow valves 940FV and921FV. As with the initial parent asset, there is no particular order asto how each equipment asset be processed, so long as all equipment isprocessed.

FIG. 11 is a diagram illustrating various aspects of an example ofdeveloping a hierarchy of intelligent agents for a Hierarchical AssetControl Application in accordance with various embodiments of thepresent disclosure.

With all equipment defined in the reactor industrial system 1100, thesystem now defines an asset instance based on the equipment list 820 andcreates intelligent agents for each smart asset. As with processing theequipment list, the system may choose any point to being the process. Inthe present example the exothermic reactor 910 is chosen and as assetinstance is defined 820 based on the characteristics defined in theequipment list. To aide in the definition of intelligent assetstemplates in an intelligent asset library 822 may be used which containsvarious intelligent asset templates to begin to characterize the smartasset. An example intelligent asset template is shown in FIG. 8C. Itshould be appreciated there is a high degree of flexibility as to theamount and type of data that can be populated into such intelligentasset templates. Smart asset/intelligent agent data for population maybe derived from operational historical use data/operationalcharacteristics, it can be provided by a component vendor, it can bederived as generic component models (e.g., a typical heat exchanger hasa reliability curve . . . ).

Once the equipment element is mapped with an intelligent asset templateand defined, intelligent asset templates components are selected 824 andan intelligent agent data structure is created for each smart asset 1110in preparation to populate other characteristic data. In the presentexample, an intelligent asset template corresponding to an exothermicreactor is created and populated with available information which mayinclude, but not limited to, the exothermic reactor name, category,model, serial number, and the type of application the exothermic reactorwill be used in. This data is made available on the initial processingof the equipment list and populated into the intelligent asset datastructure on selection and creation of the intelligent asset datastructure specifically for the exothermic reactor 910.

As the smart asset and the industrial application has been identified, acharacterization of the constraints of the asset 828, in this examplethe exothermic reactor 910, is defined. While various constraints mayexist, in the specific example, safety, environmental, and reliabilityconstraints are contemplated. An example a safety constraint may be theexothermic reactor may only be operated to an absolute maximum of 500MPa as indicated by the pressure gauge 910PI. Violating this constraintmay lead to catastrophic failure of the exothermic reactor 910 andpresent a substantial safety hazard. An environmental constraint examplemay involve the temperature of the water in the exothermic reactorjacket 911 due to an exothermic reaction in the reactor. For example,warm water from the jacket may only be expelled to the local watersupply at a temperature of 100 Fahrenheit as indicated by temperaturegauge 910TI. Failure to adhere to this operational risk constraint maylead to regulatory penalties and harm to a local environment. Finally, areliability constraint example may be developed such that the exothermicreactor 910 has a maximum of 100,000-hour service life for supportingcontinuous material processing. Exceeding this constraint may lead toreduced efficiency of the exothermic reactor 910 and/or risk of reactorelement operational failure, both of which would impact control,management and optimization of the Integrated Smart Asset Control Systemand the process associated with the Integrated Smart Asset ControlSystem. Each of these, and any other constraints, are populated into theintelligent asset data structure 832 for the given equipment element, inthis example, the exothermic reactor 910. This, now complete, exothermicreactor intelligent agent data structure may be stored into anintelligent asset database 834. This process of creating intelligentagent data structures is repeated for all equipment, such as the“Product C” pump 913 and valve 913FV equipment grouping, heat exchangerequipment grouping 1020, and “Reagent B” equipment grouping 1030.

Once all constraints have been calculated and a smart asset basis, anyoptimization for any variable defined by the system or a user of thesystem, such as real time operating profit is derived and the creationof an optimization for each asset 1120 is performed and stored in theintelligent agent for each smart asset. This process continues untilintelligent asset data structures have been created for each element ofphysical equipment and a complete system/application asset list exists838 for the reactor industrial system 1100 and each smart asset has apopulated corresponding intelligent asset with relative constraints,objectives, and optimization solution 1130.

FIG. 12 is a diagram illustrating various aspects of an example ofsimulation and validation for a Hierarchical Asset Control Applicationand corresponding control hardware in accordance with variousembodiments of the present disclosure.

This validation may include both the validation of the smart assetsthemselves as well as the hierarchy formed by the composition of smartassets. As the complete system/application smart asset list exists 842for the reactor industrial system 1200, each parent/child assetcommunication and control path is validated 844. Various methods existto validate such paths including but not limited to intra-assetcommunication such as data packet “pings” or other more formal, protocolbased handshakes such as, but not limited to, TCP/IP addressing.Validation of the example industrial system may include utilizing theasset list to begin the validation and determining each parent cancommunicate to each child. In the present example, the heat exchanger920 which controls the temperature of the cooling water pumped to thereactor jacket 911 will validate its children assets includingtemperature indicator 920TI to provide a measurement of the temperatureof the cooling water, flow indicator 920FI which provides a measurementof the flow rate of cooling water, valve positioner 920FV controls theflow of cooling water, and valve positioner 921FV controls the flow rateof the chilled water to the heat exchanger. This process continues untilthe entire smart asset list has been evaluated validated both as anindividual smart asset and communication within the control hierarchyformed by the collection of assets as the Hierarchical Asset ControlApplication. A query is made by the system to determine if any issues oranomalies exist in the parent/child validation 846. If any issues exist,they are remediated 848 and once the remediation is completed 852, or ifno issues existed, the process continues to a step of simulating thecomplete validated hierarchical industrial system/application.

Given the example of a complete validated industrial asset hierarchicalstructure for the reactor industrial system 1200, prior to controlsystem commissioning, simulation of the Hierarchical Asset ControlApplication 1220 may be performed to further confirm the proposedindustrial solution will behave as expected when instantiated inhardware. If a simulation of the Hierarchical Application ControlApplication is being performed 1230 and an issue is determined to exist,the issue may be presented for remediation. For example, the pump 985responsible for providing chill water from the chiller and reservoir 980forming a chiller asset 1210 is not of an appropriate type forcharacteristics such as flow rate, physical size, or any otherparametric as determined by the system or a user of the system, theissue may be remediated 856, prior to deployment of a hardware system.

Such remediation may include, but is not limited to, adjusting the assettemplate for the particular asset, adjusting the intelligent asset datastructure to accommodate for other considerations, or replacing theparticular equipment, in this case the pump 985 with a piece ofequipment more suitable for the industrial application. Once thisreplacement has been completed, the entire process of determining theequipment list 710, selecting one or more asset templates 720, creatingan intelligent agent data structure 730, developing the intelligentagent 740, and validating and simulating the complete system should berepeated to thoroughly validate the entire Integrated Smart AssetControl System.

Once the completely validated Hierarchical Asset Control Application isavailable, hardware instantiation of the supporting control system maybe created 1240, deployed, and integrated with the underlying equipmentelements in conjunction with any control hardware required to transformthe Hierarchical Application Control Application as described in FIG. 8Eto an Integrated Smart Asset Control System an example as illustrated inFIG. 5B.

The extended control aspects of the Hierarchical Asset ControlApplication actually partition into two general categories of real-timecontrol. First is providing real-time control for the basis objectivesof the business which may include, but are not limited to,profitability, operational profitability, operational efficiency andasset reliability. The second is providing real-time control on thedynamic constraints on the objectives such as, but not limited to,safety-risk, environmental risk and security rules. The diagram depictedin FIG. 13 displays an example of a basic system model optimizationfunctionality for the objectives and dynamic constraints of aHierarchical Asset Control Application. Each box in the diagramrepresents one aspect of the overall control system. The “ProcessControl for Operational Efficiency Improvement” involves process controlas associated with the target asset. Each of the other functions will bedescribed individually in reference to FIGS. 13 and 3B in thedescription below.

Control System Components

The extended control aspects of the Hierarchical Asset ControlApplication can be partitioned into two general categories of real-timecontrol. For example, providing real-time control for the basicobjectives of a business objectives such as profitability, operationalprofitability, operational efficiency and asset reliability can be onecategory. The second category can be providing real-time control on thedynamic constraints on the objectives such as safety risk, environmentalrisk and security rules. The diagram depicted in FIG. 13 illustrates anexample of a control system model for the objectives and dynamicconstraints of the system. Each block in the diagram represents oneaspect or component of the overall control system. FIG. 3B illustratesexample components of an intelligent agent 325 in a CPS associated witha smart asset or smart asset set or other smart asset grouping forproviding real-time control of the basic objectives of a business anddynamic constraints of objectives (i.e., real-time control overprofitability, reliability, process efficiency, safety risk, environmentrisk and security rules). In some embodiments, the intelligent agent 325can include a Real Time (RT) profitability controller 330, an RT processefficiency controller 335, an RT reliability controller 355, an RTenvironmental risk controller 345 and an RT safety risk controller 350.It should be noted that in some embodiments, the intelligent agent 325may include more or less controllers, thereby providing control overmore or less domains. In some embodiments, a coordinator module 340 maybe included in the intelligent agent for coordinating the execution ofthe controllers according to a control hierarchy. In yet otherembodiments, the intelligent agent 325 can include other modules besidesthe RT controllers. Examples of such modules can include but are notlimited to: a historization module 365 and a small data analytics engine370.

Referring to FIG. 3B, the intelligent agent 325 can include othermodules such the historization module 365 and the small data analyticsengine 370 in some embodiments. The historization module 365 cangenerate historical data associated with an asset or asset set. Suchhistorical data can include sensor measurements, set points, deviationsfrom set points, corrective actions taken, and/or the like, and canprovide a snapshot of the state or health of the asset or asset set at apoint in time. The historical data can be reported to higher level assetsets (e.g., for calculations) and/or exported to other systems such asthe big data analytics engine 2205 described in reference to FIG. 22.The small data analytics engine 370 can locally collect and analyze dataassociated with the asset or asset set and report results to the bigdata analytics engine 2205 of FIG. 22 directly or through a higher levelintelligent agent.

Referring to FIG. 13, block 1315 represents the process control functionas provided by automation systems for process control as associated withthe target asset. The process control function can be implemented by theRT process efficiency controller 335 depicted in FIG. 3B. Each of theother functions will now be described individually in the descriptionsbelow.

I. Real-Time Profitability Control for Improved OperationalProfitability

In some embodiments, real-time profitability control function can bepartitioned into two components corresponding to blocks 1305 and 1300 asshown in the model depicted in FIG. 13. These control functions at thesmart asset or smart asset set level, which may be represented by anindustrial asset base 1325, or other structure defined herein includingHierarchical Asset Control Applications or Integrated Smart AssetControl Systems, may be implemented by the RT profitability controller330, specifically the business profitability controller 330 a andoperational profitability controller 330 b depicted in FIG. 3B. As usedherein, operational profitability is defined as the profitabilitycreated for a business based on decisions made within the industrialoperation, while business profitability is defined to be theprofitability created for the business based on decisions made above theindustrial operation. The operational profitability functions are guidedby the business profitability functions.

The business profitability control function 1300 represents controldecisions that involve business decisions at a level above theoperations. These decisions may include things such as production toschedule based on contractual commitments. For example, a contractcommitment may involve the production of a defined amount of product bya defined delivery date. The contract may have been accepted at a pointof time in which meeting the commitment within normal productionoperations did not present a significant risk. If something were tohappen in the operation that increases the risk that the contract maynot be fulfilled, decisions may need to be made at this level to controlthe response. For example, if the system determines that the reliabilityof an asset required to fulfill production schedules is declining at anunexpected rate and that the smart asset may need maintenance, adecision at this level may need to be made to determine whether the bestbusiness decision would be to risk continuing operating until thecommitment is reached, or to shut down for maintenance. This controldecision would typically be made manually (manual control) today byhighly experienced production managers, but may be able to be madeautomatically (automatic control) once enough knowledge has been gainedthrough experience with the new performance measures of the operation. Asuitable automatic control system can be an expert system that emulatesthe decision processes of the best production managers. The output ofthe business profitability control function 1300 can cascade to thereal-time profitability control function of the operation 1305 andtogether these controls can provide guidance (set points,automatic/manual etc.) to the real-time profitability controlstrategies.

In some embodiments, it is useful to consider real-time profitabilitycontrol as the primary loop of a cascade control strategy with thesecondary loops being the process control loops traditionally associatedwith improved operational efficiency. From that perspective, the outputsof the real-time profitability controllers will be the set points of thecontrollers designed for operational efficiency improvements. Each setpoint in a process control system needs to be set at a particular valueor within a range of values for the operation to be maximizingoperational profitability. The real-time profitability controllers canset those set points accordingly.

From a manual control perspective, the way in which real-timeprofitability control is accomplished is that the operator responsiblefor setting the set points of the controllers of the operation views thereal-time profitability measures for that operation and modifies the setpoints within the allowable values for each. The operator then looks atthe real-time profitability measures to determine whether theyincreased, decreased or stayed the same. If they increased, the operatorwill continue to change the set points in the direction that caused theincrease. If they decreased the operator will back off the set points toreverse the decrease, and if they did not change the operator may trysome other set points. Over time the operators will learn which setpoints provide the best benefit during each phase of operation and willprioritize the set point manipulations accordingly. Since the real-timeprofitability of the process is dynamic, the operators shouldcontinually modify set points to determine if there are continualopportunities for improvements.

An automatic real-time profit controller can be developed by automatingthe operator approach via expert systems technologies. Over time andexperience with manual profitability control the appropriate set pointpriorities, values and directions of change can be determined fordifferent phases of the operation. The expert system controller can bedeveloped to directly and automatically perform the most profitable setpoint modifications continuously and may be implemented using componentsillustrated in FIG. 28.

Alternatively, an integral only controller may be implemented for thereal-time profitability measures associated with each part of theoperations. The set point to the real-time profitability controller canbe set to a high range based on prior real-time profitabilityexperience. The output of the integral controller can be set up to drivean incremental signal based on the deviation from set point that signalcan be attached to each of the process controller set points in adirectionally compensated manner that will either add or subtractincremental values from each process control set point to drive theprofitability in the appropriate direction. Integral control is asuitable choice in this case because it does not require a physicallydetermined natural period of the control loop to work correctly. Somereal-time profit control loops may not be constrained to a constantperiod since they may be human transaction determined.

It is to be understood for the purposes the following examples asillustrated by the figures, the features and functionality are achievedand executed by an operational Integrated Smart Asset Control Systemdeveloped from a Hierarchical Assets Control Applications andcorresponding control hardware developed for the underlying equipmentelements.

Real-time profitability measurement is designed to determine thereal-time component profit impact from a smart asset or smart asset setor other smart asset groupings in an industrial operation over a finitetime period. In some embodiments, this measurement can be defined by thefollowing example equation:RT Profitability=Production Value−(Consumed Energy Cost*EnergyConsumption+Material Cost*Material Consumption)Δt

Wherein, Production Value is the current value of the products producedtimes the amount of product produced over the time frame inconsideration (zit). The value of the products produced can beestablished in a number of ways depending on the client. For example,the value of the products produced can be the transfer price if theclient utilizes a transfer pricing mechanism. By way of another example,the value may be the current market value of the products when they areproduced even though they may not actually be sold at that value. It isup to the client as to how this variable should be implemented sincethis is a human-based, not science-based decision. Consumed Energy Costis equivalent to the energy cost at time of consumption times the amountof energy consumption. Material Cost is equivalent to material cost attime of consumption times the amount of material consumed.

II. Real-Time Asset Safety Risk Constraint Determination

FIG. 14A is a block diagram and FIG. 14B is a logic flow diagramillustrating determining a safety risk constraint related to assets in aHierarchical Asset Control Application in accordance with variousembodiments of the present disclosure.

Safety risk is the measurement of the probability that an asset may beinvolved in an immanent safety incident multiplied by the expectedseverity of the safety incident which is based on either pastexperience, extrapolation, or a combination of both. Safety riskmeasurement as defined may be very similar to other constraintmeasurement, such as reliability measurement.

A primary distinguishing aspect of safety risk is the severity ofassociated consequences. Reliability failures may shutdown and damageequipment, but the expectation of the severity of the damage is below asafety threshold. Safety risk implies that the degree of equipmentdamage may be higher and that there may be risk to people and the plantfacility. Each industrial operation may assess the consequence levelnecessary to consider a risk to be a safety risk as compared with merelya reliability risk.

An overall RT safety risk for a Hierarchical Asset Control Applicationmay be defined by the maximum of a derived Reliability Safety Risk,Operational Safety Risk, and Conditional Safety Risk. A maximum value ofReliability Safety Risk, Operational Safety Risk, and Conditional SafetyRisk is selected as it provides a method to qualify the most significantrisk of the three associated with the operational process at a giventime.

A reliability safety risk input module 1402 determines the ReliabilitySafety Risk and is a value determined by the relationship:Safety Risk=MAX(p ₁ S(t)*E ₁ ,p ₂ S(t)*E ₂ , . . . ,p _(n) S(t)*E _(n)).

Wherein: pS_(i)(t) is the probability of safety incident I occurring intime t, and E is the expected consequence if the incident I occurs.

There are two basic aspects to the type of safety incidents that mayhappen in a Hierarchical Asset Control Application. First, is a failureof an asset that leads to a safety incident. Second is the failure of aprocess corresponding to an asset or set of assets that might lead to asafety failure.

In the first instance the p_(i)S(t) term is essentially equivalent tothe probability of equipment failure from a reliability measurementperspective. The E_(i) term is the expected consequence level that willresult from such a failure. As safety risk is generally a criticalconcern in industrial operations, it may be appropriate and advisable toset E_(i) to the maximum expected consequence associated with thedefined safety failure. It should be appreciated that other values areable to be set and are contemplated as part of this disclosure. Adecision to set E_(i) to either the expected consequence level of themaximum consequence level is the responsibility of plant management, orother party. A safety risk measurement approach and calculation willwork the same way in either case. Once an asset reliability measurementassessment has been completed for the smart asset, the smart assetsafety risk measurement assessment will be fairly straight forward todevelop. The only information that will need to be added is the value ofthe expected consequence for each potential safety risk incident.

The second type of safety failure results from a process safety failurewhich may involve multiple simultaneous variables changing whichtogether result in a safety incident. The probability of a processsafety incident occurring within a smart asset or smart asset set, orother smart asset grouping can be determined by analyzing past incidentsthat have occurred through use of historic data within the present orsimilar assets to identify the combination of lead indicators of pastsafety incidents. Post-analysis, utilize any lead indications todetermine if current operational conditions in the asset signal theimmanent probability of a repeated process safety incident of a similartype.

This determination may be accomplished through the utilization ofreal-time workflows designed to recognize the lead indicators. Thisshould be done for all past process safety incidents associated with theasset. The pS_(i)(t) for process safety risks can be established bydetermining the average time between past lead indications and theoccurrences of the safety incidence and extrapolating that time to the tin the probability analysis. It may be appropriate when dealing withsafety risk analysis, to set the probability to a high value immediatelyupon occurrence the lead indicators. This is typically theresponsibility of plant management or other responsible party. It shouldbe noted that there may not be enough safety incidences in aHierarchical Asset Control Application to get a base for comprehensiveprocess safety risk analyses. In these cases, the analysis of safetyincidences that have occurred in other similar or analogous operationsmay help to provide the base of historical information necessary forreliable analysis. A library of such similar or analogous HierarchicalAsset Control Application control application safety data may beavailable from a variety of sources.

Effectiveness of the safety risk measurement will correlate to the valueof the expected consequence of a safety incident, E_(i). An expectedvalue of the consequence of a safety incident, E_(i), is a numericindicator of the extent of damage, injury or death which may be expectedfrom an incident of the type predicted. Since severity is a combinedqualitative and quantitative metric, E_(i) could be expressed as anormalized value in order to establish a stronger relative assessment ofthe safety risk across potential incidents. It may be appropriate forthe E_(i) to be the greatest potential severity of the worst of the pastevents of the same type, rather than the expected severity. This maybetter represent the caution that should be applied to safety risk inindustrial operations. Again, whether expected severity of greatestpotential severity is employed is the responsibility of plant managementor other responsible party.

An operational safety risk input module 1404 determines the OperationalSafety Risk and is a value determined by characteristics such as thethoroughness and timeliness of safety inspections. These inspections maybe defined by the industrial application operator, as defined by localstandards, or equivalent regulatory agency, such as OSHA. A safety teamon site or other responsible party makes the determination as to theOperational Safety Risk.

A conditional safety risk input module 1406 determines the ConditionalSafety Risk and is a value determined by the recognition of leadindicators of potential safety incidences as determined from analysis ofpast safety incidences. If the lead indicators of a potential safetyincident are recognized, the conditional safety risk value is setaccording to the expected time and severity of the occurrence of theincident based on past experience. A safety team on site or otherresponsible party makes the determination as to the Conditional SafetyRisk.

To evaluate the overall safety risk, the process will receivereliability safety risk information 1410, operational safety riskinformation 1411, and conditional safety risk information 1412. Thisinformation may be input from a user to the system, or from the systemitself in real time, from a storage device to the system 1403, or from arepository outside of the system such as the cloud.

With these inputs and that of the conditional safety risk workflowmonitor 1405, the system will determine the Reliability Safety Risk1413, Operational Safety Risk 1414, and Conditional Safety Risk 1415 asdefined above. An overall RT safety risk for a Hierarchical AssetControl Application is evaluated 1416 by the maximum of a derivedReliability Safety Risk, Operational Safety Risk, and Conditional SafetyRisk.

As a result of the evaluation of the overall RT safety risk 1416 willanalyze the output of the overall safety risk 1408 and apply any actionrules to be evaluated 1417 based on the overall RT safety risk. Anotification module 1407 will provide notification 1419 to a user of thesystem or the system itself and a workflow trigger and corrective actionmodule 1409 will trigger workflows 1418 proscribed for the system tomaintain or remediate conditions observed and determined.

This overall RT safety risk 1416 may be used by the real-time safetycontrol function 1320 and provides a constraint to both real-timeefficiency control 1315 and real-time profitability control 1305functions. The control function for real-time safety risk involves twoapproaches. First is the safe control of the production process througheffective process control strategies designed to control the processwithin safe limits. Second is evaluating the current real-time safetyrisk and the change in safety risk over a defined time period associatedwith each asset and asset set to determine whether the safety riskprofile reaches an unacceptable level. The unacceptable level of safetyrisk can be defined by the client (e.g., management and professionalteam of the operation). If the safety risk controller determines thatthe safety risk is approaching an unacceptable level, the controller candetermine the appropriate action and send a message to the appropriatecontrollers of the process control system and/or the real-timeprofitability control system for action. For example, if the safety riskis approaching an unacceptable level, the initial safety risk controlaction may be to send a message to the real-time profitability controlfunction, which may take actions to slow down production in order toreduce the real-time safety risk. If the real-time safety risk is morecritical, the safety risk control action may be to send a controlmessage to the process control system to either directly reduceproduction by altering set points in order to reduce the safety risk, orshutdown parts or all of the process to avoid a safety incident, or somecombination of the two.

In some embodiments, real-time safety risk control can be implemented bythe RT safety risk controller 350 depicted in FIG. 3B. In someembodiments, the controller for real-time safety risk control can bebased on an expert system or automatic workflow that emulates theresponse of a safety expert and sends the appropriate outputs to eitherthe real-time profitability control function, or the process controlfunction, or both as appropriate. In other embodiments, the real-timesafety risk control can be implemented manually, for example, by asafety professional.

III. Real-Time Asset Environmental Risk Constraint Determination

FIG. 15A is a block diagram and FIG. 15B is a logic flow diagramillustrating determining an environmental risk constraint related toassets in a Hierarchical Asset Control Application in accordance withvarious embodiments of the present disclosure.

Environmental risk is a special case of safety risk in which theexpected damage might occur outside of the plant borders. Environmentalrisk is the measurement of the probability that an asset will beinvolved in an immanent environmental incident multiplied by theexpected severity of the environmental incident based on either pastexperience, extrapolation, or a combination of both.

An overall RT environmental risk for a Hierarchical Asset ControlApplication may be defined by the maximum of a derived ReliabilityEnvironmental Risk, Operational Environmental Risk, and ConditionalEnvironmental Risk. A maximum value of Environmental Safety Risk,Operational Safety Risk, and Conditional Safety Risk is selected as itprovides a method to qualify the most significant risk of the threeassociated with the operational process at a given time.

A reliability environmental risk input module 1521 determines theReliability Environmental Risk and is a value determined by therelationship:Environmental Risk=MAX(p ₁ E(t)*E ₁ ,p ₂ E(t)*E ₂ , . . . ,p _(n) E(t)*E_(n))

Wherein: pE_(i)(t) is the probability of environmental incident Ioccurring in time t and E_(i) is the expected consequence if theincident I occurs.

There are two basic aspects to the type of environmental incidents thatmay happen in an industrial operation. First is a failure of an assetthat leads to an environmental incident. Second is the failure of aprocess across an asset or set of assets that might lead to anenvironmental failure.

In the first instance the piS(t) term is essentially equivalent to theprobability of equipment failure from a reliability measurementperspective. The Ei term is the expected consequence level that willresult from such a failure. As environmental risk is generally acritical concern in industrial operations, it may be appropriate andadvisable to set Ei to the maximum expected consequence associated withthe defined environmental failure. It should be appreciated that othervalues are able to be set and are contemplated as part of thisdisclosure. A decision to set Ei to either the expected consequencelevel of the maximum consequence level is the responsibility of plantmanagement, or other party. An environmental risk measurement approachand calculation will work the same way in either case. Once an assetreliability measurement assessment has been completed for the asset, theasset environmental risk measurement assessment will be fairly straightforward to develop. The only information that will need to be added isthe value of the expected consequence for each potential environmentalrisk incident.

The second type of environmental failure results from a processenvironmental failure which may involve multiple simultaneous variableschanging which together result in an environmental incident. Theprobability of a process environmental incident occurring within anasset or asset set can be determined by analyzing past incidents thathave occurred through use of historic data within the present or similarassets to identify the combination of lead indicators of pastenvironmental incidents. Post-analysis, utilize any lead indications todetermine if current operational conditions in the asset signal theimmanent probability of a repeated process environmental incident of asimilar type.

This determination may be accomplished through the utilization ofreal-time workflows designed to recognize the lead indicators. Thisshould be done for all past process environmental incidents associatedwith the asset. The pS_(i)(t) for process environmental risks can beestablished by determining the average time between past leadindications and the occurrences of the environmental incidence andextrapolating that time to the tin the probability analysis. It may beappropriate when dealing with environmental risk analysis, to set theprobability to a high value immediately upon occurrence the leadindicators. This is typically the responsibility of plant management orother responsible party. It should be noted that there may not be enoughenvironmental incidences in a Hierarchical Asset Control Application toget a base for comprehensive process environmental risk analyses. Inthese cases, the analysis of environmental incidences that have occurredin other similar or analogous operations may help to provide the base ofhistorical information necessary for reliable analysis. A library ofsuch similar or analogous Hierarchical Asset Control Applicationenvironmental data may be available from a variety of sources.

Effectiveness of the environmental risk measurement will correlate tothe value of the expected consequence of an environmental incident,E_(i). An expected value of the consequence of an environmentalincident, E_(i) is a numeric indicator of the extent of damage, injuryor death which may be expected from an incident of the type predicted.Since severity is a combined qualitative and quantitative metric, E_(i)could be expressed as a normalized value in order to establish astronger relative assessment of the environmental risk across potentialincidents. It may be appropriate for the E_(i) to be the greatestpotential severity of the worst of the past events of the same type,rather than the expected severity. This may better represent the cautionthat should be applied to environmental risk in industrial operations.Again, whether expected severity of greatest potential severity isemployed is the responsibility of plant management or other responsibleparty.

An operational environmental risk input module 1523 determines theOperational Environmental Risk and is a value determined bycharacteristics such as the thoroughness and timeliness of environmentalinspections. These inspections may be defined by the industrialapplication operator, as defined by local standards, or equivalentregulatory agency, such as OSHA or the EPA. An environmental team onsite or other responsible party makes the determination as to theOperational Environmental Risk.

A conditional environmental risk input module 1525 determines theConditional Environmental Risk and is a value determined by therecognition of lead indicators of potential environmental incidences asdetermined from analysis of past environmental incidences. If the leadindicators of a potential environmental incident are recognized, theconditional environmental risk value is set according to the expectedtime and severity of the occurrence of the incident based on pastexperience. An environmental team on site or other responsible partymakes the determination as to the Conditional Environmental Risk.

To evaluate the overall environmental risk, the process will receivereliability environmental risk information 1530, operationalenvironmental risk information 1531, and conditional environmental riskinformation 1532. This information may be input from a user to thesystem, or from the system itself in real time, from a storage device tothe system 1522, or from a repository outside of the system such as thecloud.

With these inputs and that of the conditional environmental riskworkflow monitor 1524, the system will determine the ReliabilityEnvironmental Risk 1533, Operational Environmental Risk 1534, andConditional Environmental Risk 1535 as defined above. An overall RTEnvironmental risk for a Hierarchical Asset Control Application isevaluated 1536 by the maximum of a derived Reliability EnvironmentalRisk, Operational Environmental Risk, and Conditional EnvironmentalRisk.

As a result of the evaluation of the overall RT Environmental Risk 1536will analyze the output of the overall environmental risk 1527 and applyany action rules to be evaluated 1537 based on the overall RTenvironmental risk. A notification module 1526 will provide notification1539 to a user of the system or the system itself and a workflow triggerand corrective action module 1528 will trigger workflows 1538 proscribedfor the system to maintain or remediate conditions observed anddetermined. An environmental risk analysis measurement will be indicated1540.

Real-time environmental risk can be considered a special case ofreal-time safety risk. Real-time environmental risk control function1320 provides a constraint to both real-time efficiency control 1315 andreal-time profitability control 1305 functions. The control function forreal-time environmental risk can involve two approaches. First is thecontinuous control of environmental emissions (gas, liquid and solid)from the process by the application of effective process controlstrategies designed to control emissions within safe limits. Thisinvolves direct measurements of emissions in gas and liquid streams, andto a lesser degree to solid waste. Second is evaluating the currentreal-time environmental risk and the change in environmental risk over adefined time period associated with each asset and asset set todetermine whether the environmental risk profile is approaching anunacceptable level. The unacceptable level of environmental risk can bedefined by the enterprise (e.g., management and professional team of theoperation) and/or external agencies. In some embodiments, the real-timeenvironmental risk control function 1320 can be implemented by the RTenvironmental risk controller 345 depicted in FIG. 3B. If theenvironmental risk controller determines that an unacceptable level isbeing encountered, it ascertains the appropriate action and sends amessage to the appropriate controllers of the process control systemand/or the real-time profitability control system for action. Forexample, if the environmental risk is approaching an unacceptable level,the initial environmental risk control action may be to send a messageto the real-time profitability control function, which may take actionsto slow down production in order to reduce the real-time environmentalrisk. If the real-time environmental risk is more critical, thecontroller may send a control message to the process control system toeither directly reduce production by altering set points in order toreduce the environmental risk, or shutdown parts or all of the processto avoid a safety incident, or some combination of the two.

In some embodiments, the RT environmental risk controller can be basedon an expert system or automatic workflow to emulate the response of anenvironmental expert and send the appropriate outputs to either thereal-time profitability control function, or the process controlfunction, or both as appropriate. In other embodiments, the real-timeenvironmental risk control function can be implemented manually, forexample, by an environmental professional.

In both conditional and operational environmental incident analyses, theprobabilities are a function of the specific asset with respect to theenvironmental incident potential.

Potential severity of the incident can be determined by the severity (interms of cost, damage, injury and death) of the worst of the past eventsof the same type. Since the specific measures of injury and death costsare subjective, the potential severity may be normalized to includethese qualitative factors.

IV. Real-Time Asset Reliability Risk Constraint Determination

FIG. 16A is a block diagram and FIG. 16B is a logic flow diagramillustrating determining a reliability risk constraint related to assetsin a Hierarchical Asset Control Application in accordance with variousembodiments of the present disclosure.

An overall RT reliability risk for a Hierarchical Asset ControlApplication may be defined by the maximum of a derived Reliability Risk,Operational Reliability Risk, and Conditional Reliability Risk. Amaximum value of Reliability Safety Risk, Operational Safety Risk, andConditional Safety Risk is selected as it provides a method to qualifythe most significant risk of the three associated with the operationalprocess at a given time.

There are two real-time measures that provide different aspects of thecurrent reliability of the asset under consideration. First is the“Probability of Asset Failure” (pF), defined as the probability that theasset will fail over the time period t. The second is “Maintained Stateof the Asset” (MS), defined as the measure of the maximum assetperformance in current state versus the ideal maximum performance. Thesetwo measures provide different, but related measures of the reliabilityof an asset and both can be controlled to improve the overall assetreliability. Each asset or asset set must be modelled based on itsspecific characteristics, yet there are common factors across assets andasset sets that serve as the basis for the Real-Time Reliabilitycalculation as defined by the relationship:MS=Current performance of the asset/Expected performance of the asset

The current performance of the asset can be measured as a function ofthe work output of the asset as compared to what would be expected ifthe asset were operating appropriately. The expected performance of theasset can be determined by analyzing the energy and/or material inputsto the asset and determining what the output of the asset should be ifthe asset was operating to design. For base equipment assets, such aspumps, motors, compressors and pipes, the expected performance of theasset should be determined by the equipment manufacturer throughperformance curves developed at the design or testing stages of theequipment development. These performance curves can be embedded insoftware models for the equipment and the models can be used todetermine the actual expected performance of the asset during operation.

Since the performance of any asset may not be linear over its operatingrange, the Maintained State of the asset may be different at differentpoints along its operating range. Additionally, the performance of theasset may vary according to the type of materials being processedthrough the asset, such as highly corrosive materials, or highly viscousmaterials, or the like. In these cases, there may be differentperformance curves associated with different material characteristics.

For more complex asset sets comprised of multiple base equipment assets,such as a process unit or a work cell, the maintained state may be ableto be determined much in the same manner as for the base equipmentassets. In this case it may be necessary to utilize the design modelsused at the design phase of the plant to determine the expected value ofthe performance of the asset set.

A reliability risk input module 1641 determines the Reliability Risk andis a value determined by the relationship:Reliability Asset Failure=MAX(p _(ms) f(t),p _(c1) f(t), . . . ,p _(cn)f(t))

Wherein: pmsf(t) is the probability of a failure due to the maintainedstate of the asset and pcif(t) is the probability of a failure due tothe measured condition i. The measured conditions include variables suchas temperature, operating time, number of starts, and/or othersassociated with an asset.

A probability of failure is determined by the relationship:pf(t)=1−e ^(ut)

Wherein u is the failure rate over time t.

For base equipment assets, the probability of failure based on each ofthe measured asset conditions, including the maintained state as aspecial condition, would be determined from the equipment manufacturesspecifications and testing information. Most equipment manufacturersdetermine such information during design and testing. Unfortunately,this information is not commonly used to determine the probability offailure in real time. This information can be loaded into theprobability of asset failure analysis module in the form of models.These equipment models will be analyzed and compared with the currentoperating conditions to determine the probability of failure resultingfrom each condition. The overall probability of failure for the assetwill be determined as the maximum of the condition-based failureprobabilities.

In some equipment there may be additional probability of failures basedon the process being performed in the equipment that may not be able tobe determined by individual condition-based probability of failure,rather may need to be determined by analyzing combinations ofconditions. In these instances, the equipment manufacturer may havecombinational models that might be used to determine the probability offailure from multivariable equipment process conditions. Alternatively,combinational process failures may be able to be predicted by analyzingpast incidents that have occurred within this or similar operatingassets to identify lead indicators of past failures then utilizing thoselead indications to determine if current operational conditions in theasset signal the immanent probability of a failure.

For higher level assets (unit, work cell, area, plant, enterprise) avery similar probability of failure analysis can be developed byanalyzing the maintained probabilities of failure of each of theequipment asset (or next lower level assets) that are part of thehigher-level asset and determining the probability of failure of thehigher-level asset to be the maximum of the probabilities of failure ofthe next lower level assets.

There may be process conditional probabilities of failures in higherlevel assets much in the same manner as there are in equipment assets.These may be determined in a very similar manner to the probability ofprocess-based failures for base equipment assets. Data from processhistorians should help to identify the probabilities of process-basedfailures through utilization of the leading indicators of past failuresof the same type.

Since direct real-time measurements of reliability of the type describedherein have not traditionally been made for industrial assets, there isno history of measurement response and interactions for any specificindustrial assets at this point in time. It is fully expected that withtime and history the more specific equation will be developed forindividual assets and asset classes that will be able to be genericallyapplied. Wherein:

An operational inspection risk input module 1643 determines theOperational Reliability Risk and is a value determined bycharacteristics such as the thoroughness and timeliness of reliabilityinspections. These inspections may be defined by the industrialapplication operator, as defined by local standards, or equivalentregulatory agency, such as OSHA. A reliability team on site or otherresponsible party makes the determination as to the OperationalReliability Risk.

A conditional reliability risk input module 1645 determines theConditional Reliability Risk and is a value determined by therecognition of lead indicators of potential reliability incidences asdetermined from analysis of past reliability incidences. If the leadindicators of a potential reliability incident are recognized, theconditional reliability risk value is set according to the expected timeand severity of the occurrence of the incident based on past experience.A maintenance team on site or other responsible party makes thedetermination as to the Conditional Reliability Risk.

To evaluate the overall reliability risk, the process will receivereliability risk information 1650, operational reliability riskinformation 1651, and conditional reliability risk information 1652.This information may be input from a user to the system, or from thesystem itself in real time, from a storage device to the system 1642, orfrom a repository outside of the system such as the cloud.

With these inputs and that of the conditional reliability risk workflowmonitor 1644, the system will determine the Reliability Risk (i.e. assetfailure risk) 1653, Operational Reliability Risk 1654, and ConditionalReliability Risk 1655 as defined above. An overall RT Reliability Riskfor a Hierarchical Asset Control Application is evaluated 1656 by themaximum of a derived Reliability Risk, Operational Reliability Risk, andConditional Reliability Risk.

As a result of the evaluation of the overall RT reliability Risk 1656will analyze the output of the overall reliability risk 1647 and applyany action rules to be evaluated 1657 based on the overall RTenvironmental risk. A notification module 1646 will provide notification1659 to a user of the system or the system itself and a workflow triggerand corrective action module 1648 will trigger workflows 1658 proscribedfor the system to maintain or remediate conditions observed anddetermined. A resulting overall RT Reliability Risk analysis measurementwill be provided 1660 to the application or users.

It should be appreciated, there are multiple ways to impact thereliability of industrial assets in real time. For example, a first waycan be to change the operating levels of the assets (e.g., slow down acompressor) in order to reduce operational degradation over time. Asecond way can be to perform specific maintenance actions that willimprove the maintained state and reduce the probability of failure(real-time reliability). Controlling real-time reliability may not be asstraight forward as controlling real-time profitability because bothoperational efficiency and operational profitability are directlyimpacted by real-time reliability and the maintained state of the asset.Therefore, controlling real-time reliability can involve a businessdecision for the operations. For example, the best action for thebusiness of the operation may be to slow down production by somepercentage in order to reduce the probability of a failure during aproduction run, even though the instantaneous real-time profitabilitymay decrease momentarily to assure a complete run.

In some embodiments, the real-time reliability control function 1310depicted in FIG. 13 considers both the real-time reliability measure andthe maintained state measure for each asset. Both can be used incombination to drive the best reliability results. In some embodiments,the real-time reliability control function 1310 can be implemented bythe RT reliability controller 355 depicted in FIG. 3B.

From a manual control perspective, the way in which real-timereliability control is accomplished is that the operator responsible forsetting the set points of the controllers of the operation views thereal-time reliability and maintained state measures for the assets ofthat operation and either modifies the set points associated with theoperational control of the asset within the allowable values for each,or requests a maintenance action, or both. If the operator determinesthat either the maintained state or real-time reliability is decliningbeyond what may be expected and desired, he or she can evaluate thecriticality of the production to production schedule and from that takean action. One action may be to slow down the process to decrease thelevel of degradation. Another action may be to schedule maintenanceaction.

In some embodiments, an automatic real-time reliability controller canbe utilized for real-time reliability control. The controller canautomate the operator approach via expert systems technologies althoughthis may be more complex than that of the automatic real-timeprofitability controller because the appropriate action is often tied toa business profitability control decision. Over time and experience withmanual reliability control the appropriate set point and maintenanceactions can be determined for the asset and can be implemented in anexpert system that automatically emulates the operator actions in asimilar manner to real-time profitability control. But optimizing theperformance of the operation may require the implementation of an evenhigher level expert's system (either automatic or manual) that acceptsreal-time reliability inputs from the real-time reliability controllerand executes a rules set to determine what the best action should be forthe business based on current production, production schedules,maintained state degradation curves and probability of failures. This isrepresented by the business profitability control function in thediagram depicted in FIG. 13. This high-level expert in the businessprofitability control function can determine that the most profitableaction for example: continue with the current production to finish thecurrent run then perform maintenance; slow down production to reduce thereal-time reliability decline in order to be able to finish the currentrun then perform maintenance; and shut down to perform maintenance thencontinue production. It should be appreciated this disclosurecontemplates other methods.

Since the performance of any asset may not be linear over its operatingrange, the Maintained State of the asset may be different at differentpoints along its operating range. As such, alternatively, the ratio ofMaximum Current Performance to Maximum Ideal Performance can be used toascertain the maintained state of the asset.

In some embodiments, at least some of the calculations relating tomeasurement of the reliability of an asset can be based on the IEC 61508standard for functional safety of electrical/electronic/programmableelectronic safety-related systems, the entirety of which is incorporatedherein by reference.

V. Real Time Asset Security Risk Constraint Determination

FIG. 17A is a block diagram and FIG. 17B is a logic flow diagramillustrating determining a security risk constraint related to assets ina Hierarchical Asset Control Application in accordance with variousembodiments of the present disclosure.

The real-time security risk control function 630 depicted in FIG. 6Acontrols cyber-security risks. Real-time security risk is a measure ofthe probability that a cyber-security incident is imminent in thecontrol environment associated with the control of an asset or assetset. In some embodiments, the real-time security risk control functioncan include determining when the probability of a cyber-security eventoccurring is high or severe and taking one or more actions (e.g.,bringing the impacted asset or asset set offline, notifying apersonnel). The real-time security risk control function can beimplemented by the RT security risk controller 675 depicted in FIG. 6Bin some embodiments.

A security risk input module 1761 determines the Security Risk and is avalue determined by the relationship:RT Cyber-Security Risk=f(number of unexpected/unidentifiedcyber-inputs)Δt

Since the expected number of unexpected/unidentified cyber-inputs is 0,the p(cyber-security incident) can be set high (>50%) on the occurrenceof any unexpected/unidentified incidents and severe (>90%) on multipleunexpected/unidentified cyber-inputs over a given time period. Thetuning parameter can be the time period.

In some embodiments, the security risk may be determined using aninternal Intrusion Detection System (IDS), external information fromsources such as the Department of Homeland Security, private securitycompanies, or a combination thereof. An internal IDS may provide metricsabout frequency of external attack attempts and any penetration inside aprotection layer. External data may provide information about attacksoccurring elsewhere that raise a threat level.

Some non-limiting examples of control actions that can be takendynamically in response to an increased threat are: change theauthentication from a password to a combination of password andbiometric sign-in parameters; reduce, replace, and/or remove theauthorizations of individuals or roles; disable and/or enable encryptionfor data in motion and/or data at rest; change the key length forencryption; increase and/or decrease the frequency of certificatechanges on the system; change the network access permissions and/or openand/or close communication ports; disconnect part of the plant from theinternet or from other plant areas; and other measures to improve therobustness of the security layers.

Since there is little or no industrial experience utilizing real-timemetrics of the type prescribed herein, the measures can become morestandard across assets and asset classes with time and experience usingthese measures.

It should be noted that controlling these measures, similar to processcontrol for operational efficiency improvements, may involve the actualdirect control of specific measures that contribute to the high levelmetrics. For example, applying process control for operationalefficiency improvement typically involves direct control of physicalmeasures like flow, level, temperature and pressure as compared todirect control of any operational efficiency measure. In the case ofreal-time reliability control, for instance, the actual control may beon the speed of the asset under consideration (pump, compressor forexample) which will have a direct impact on the real-time reliability ofthe asset.

An operational inspection risk input module 1763 determines theOperational Security Risk and is a value determined by characteristicssuch as the thoroughness and timeliness of security inspections. Theseinspections may be defined by the industrial application operator, asdefined by local standards, or equivalent regulatory agency, such asOSHA. A security team on site or other responsible party makes thedetermination as to the Operational Security Risk.

A conditional security risk input module 1765 determines the ConditionalSecurity Risk and is a value determined by the recognition of leadindicators of potential security incidences as determined from analysisof past security incidences. If the lead indicators of a potentialsecurity incident are recognized, the conditional security risk value isset according to the expected time and severity of the occurrence of theincident based on past experience. A maintenance team on site or otherresponsible party makes the determination as to the Conditional SecurityRisk.

To evaluate the overall security risk, the process will receive securityrisk information 1770, operational security risk information 1771, andconditional security risk information 1772. This information may beinput from a user to the system, or from the system itself in real time,from a storage device to the system 1762, or from a repository outsideof the system such as the cloud.

With these inputs and that of the conditional security risk workflowmonitor 1764, the system will determine the Security Risk (i.e. assetfailure risk) 1773, Operational Security Risk 1774, and ConditionalSecurity Risk 1775 as defined above. An overall RT Security Risk for aHierarchical Asset Control Application is evaluated 1776 by the maximumof a derived Security Risk, Operational Security Risk, and ConditionalSecurity Risk.

As a result of the evaluation of the overall RT security Risk 1776 willanalyze the output of the overall security risk 1767 and apply anyaction rules to be evaluated 1777 based on the overall RT environmentalrisk. A notification module 1766 will provide notification 1779 to auser of the system or the system itself and a workflow trigger andcorrective action module 1768 will trigger workflows 1778 proscribed forthe system to maintain or remediate conditions observed and determined.A resulting overall RT Security Risk analysis measurement will beprovided 1780 to the application or users.

It should be appreciated, there are multiple ways to impact the securityof industrial assets in real time. For example, a first way can be tochange the operating levels of the assets (e.g., slow down a compressor)in order to reduce operational degradation over time. A second way canbe to perform specific maintenance actions that will improve themaintained state and reduce the probability of failure (real-timesecurity). Controlling real-time security may not be as straight forwardas controlling real-time profitability because both operationalefficiency and operational profitability are directly impacted byreal-time security and the maintained state of the asset. Therefore,controlling real-time security can involve a business decision for theoperations. For example, the best action for the business of theoperation may be to slow down production by some percentage in order toreduce the probability of a failure during a production run, even thoughthe instantaneous real-time profitability may decrease momentarily toassure a complete run.

In some embodiments, the real-time security control function 1330depicted in FIG. 13 considers both the real-time security measure andthe maintained state measure for each asset. Both can be used incombination to drive the best security results. In some embodiments, thereal-time security control function 1310 can be implemented by the RTsecurity controller 360 depicted in FIG. 3B.

Normalizing Constraint Factors for Extended Control Variables

FIG. 18A is a block diagram and FIG. 18B a logic flow diagramillustrating normalizing constraints and determining objectives relatedto assets in a Hierarchical Asset Control Application in accordance withvarious embodiments of the present disclosure.

Traditionally, optimization, whether linear or nonlinear, and whetherstatic or dynamic, has been applied to industrial operations. Theseoptimization approaches have typically been based on selecting a singleobjective function and converting all other objective functions toconstraint functions to be added to the real constraints on theobjective. The compute resources to execute these optimizers have beenextremely significant, often limiting the execution of the optimizers tohours or even days. Although very good results can be achieved from theuse of optimizers, as the speed of business has continued to increasethe optimizers have become less effective. The reason for this is by thetime the optimizer has executed and produced a result, the result nolonger matches the business situation being optimized. Therefore, theoptimizer does not optimize the objective. Secondly, as the complexityof industrial business has continually increased with multiplesimultaneous objectives, the effectiveness of single-objectiveoptimization has diminished.

As the speed and complexity of industrial business continues to increasein the future, the lack of effectiveness of traditional single-objectiveoptimizers can be a problem for industry. In accordance with the presentdisclosure, a dynamic solution to this situation can include controllingthe higher level variables in a balanced manner that will enablemultiple objectives and constraints to be simultaneously controlled tocontinuously drive optimal results. The visualization of theseobjectives can be achieved via a radar visualization of the kindsdepicted in FIG. 19A and FIG. 19B. The radar visualization enables eachperson in the operation to see the balance between the different dynamicobjectives and constraints in order to drive each of the controlstrategy in a manner that will allow them to manually manage in acontinually optimal manner.

FIG. 19A illustrates a visualization of both a current and optimalmeasure for a Hierarchical Asset Control Application with multipleconstraints and multiple objectives. In other embodiments this may be avisualization as part of a Human Machine Interface which may alsofacilitate validation, simulation, and/or other operator actions. Inthis example, while the profit objective is at or near optimal,efficiency objective, and reliability and safety constraints aresub-optimal. Further the environmental constraint is super-optimal andmay demonstrate a loss of efficiency, or even possibly a breach ofoperational boundaries.

FIG. 19B illustrates a visualization of both a current and optimalmeasure for a Hierarchical Asset Control Application with multipleconstraints and multiple objectives. In other embodiments this may be avisualization as part of a Human Machine Interface which may alsofacilitate validation, simulation, and/or other operator actions. Inthis example, profit objective is nearing the process limit as is theenvironmental risk constraint, yet both safety and reliability riskconstraints are within the process limit. Such visualizations may givean operator of a Hierarchical Asset Control Application instant feedbackon how a total system is performing relative to stated objectives andconstraints.

In some embodiments, automatic balanced control of the extended controlvariables can be implemented as experience with manual balanced controlis attained over time. The automatic balanced control can be achievedvia an expert system that emulates the actions of an expert operator inbalancing the different control variables. Such an expert system canweigh each of the control variables appropriately based on theobjectives of the operation and evaluate the error between the actualvalue and desired value for each of the control domains to determine thetrade-off actions to be performed by the automatic balanced controlsystem.

The real-time measures as defined previously can provide the data pointsfor the current situation. The ideal situation can either be staticallycalculated for the asset and used as a target for the display ordynamically calculated in the higher level Business ProfitabilityControl function 1300 described in reference to FIG. 13. In either case,the business of the operations can be in a level of real-time controlleading to real-time optimized results.

Normalization of constraints such as safety, reliability, andenvironmental, may be expressed by the following relationship:Normalized Constraints=1−[(Constraint Limit−Actual Value)/ConstraintLimit]

Normalization of real time operational profit may be expressed by thefollowing relationship:Normalized Operational Profit=1−[(Optimal Operating Profit−CurrentOperating Profit)/Optimal Operating Profit]

It should be appreciated while the foregoing examples detail Real TimeOperating Profit as the objective variable which is optimized, any otherobjective variable may be utilized for a real time operationaloptimization unit or parameter.

FIG. 19C is a diagram illustrating intersection of dynamic constraintsto form an operational boundary and optimization point of a HierarchicalAsset Control Application in accordance with various embodiments of thepresent disclosure.

In order to determine the constrained process boundaries as illustratedin FIGS. 19A and 19B, Hierarchical Asset Control Application area 1940,and an optimization point for real time operating profit 1950, thenormalized constraints such as environmental 1910, safety 1920, andreliability 1930 are utilized. It should be appreciated that theseconstraints are dynamic in nature, thereby causing both the HierarchicalAsset Control Application 1940 and optimization point for real timeoperating profit 1950, to be dynamic in nature.

FIG. 18A describes the various function block responsible for thenormalization of the various constraints and objectives. Normalizationof input data 1880, operational profitability input 1883, safety andenvironmental risk constraint data 1884, reliability risk constraintdata 1885, are evaluated. Any objective and/or constraint data to bestored or retrieved 1882 may be accessed. A notification module 1886will provide any notice to users or the system and control analysis 1887and workflow triggers 1888 will be performed as part of the analysis.

A normalization process may begin with receiving operational reliabilitynormalized information 1890 as well as normalized constraint information1891. Constraint limits 1892 on the Hierarchical Asset ControlApplication will be determined as well as the difference from thecurrent operational value to the optimum 1893. Both current and futurestate operational set points/thresholds and/or states are evaluated todetermine the necessary improvements 1894.

Once determined, the action rules are applied to more from current stateto optimal state 1895. Any necessary workflows are triggered 1896 andnotification given 1897 to the respective users or systems. Finally, thenew objective value measurement is given 1898.

From this normalization analysis of the constraints and objectives realtime knowledge of the Hierarchical Asset Control Application will beknown such as; the current operation of the application on (indicated bythe dashed line on FIG. 19B); the process limits associated with theapplication (indicated by the solid line on FIG. 19B); the operationalarea application 1940 and the optimization point for real time operatingprofit 1950.

Given the desired real time nature of determining constraints andobjectives as well as effecting changes to potentially largeHierarchical Asset Control Applications, a tiered approach isillustrated to allow robust monitoring and processing of assets deployedin the application to derive real time objectives and constraints.Further to effect control to migrate the application from the currentstate to the optimal state, control of the assets is also deployed in atiered fashion.

FIG. 20A is a block diagram and FIG. 20B a logic flow illustrating riskconstraint communication structure from asset to set, and set to unit,(or other cross smart asset grouping communication), for a HierarchicalAsset Control Application associated with an Integrated Smart AssetControl System in accordance with various embodiments of the presentdisclosure. Depending on the nature of the implementation smart assetsmay be grouped in control in any number of constructs based what isappropriate for a particular application. For example, assets groups maybe formed across multiple levels of the smart asset hierarchy.

Risk constraints are determined (as discussed above in this application)at the smart asset level of the hierarchy. Smart assets includeequipment, or groups of equipment which perform a function according tothe Hierarchical Asset Control Application and control hardware thatform an Integrate Smart Asset Control System. An example of which may bea reactor vessel and associated sensors, such as a temperature sensor.FIG. 20A illustrates a number of smart assets 2020, 2022, 2024, 2026.These smart assets will themselves, utilizing an intelligent agentdetermine any constraints relative to them. It should be appreciatedthat what constraint is relevant to what smart asset is based on theasset characteristics as well as the application, among other things.Each of these smart assets 2020, 2022, 2024, 2026 are processed at theasset level of the hierarchy 2030. It is determined at that time whatconstraints apply to each individual asset 2032.

Once constraints are determined, each asset transmits data to theirparent asset at the set level of the hierarchy 2034. Communication pathsat the asset level 2021, 2023, 2025, 2027 and parent/child relationshipshave been previously developed during the creation of an Integrate SmartAsset Control System. A validation is conducted to assure all smartassets have been processed at the asset level 2036.

Once complete, smart assets at the set level 2010, 2014 are processed2038 to determine their individual constraints 2040. In turn these setsmart assets transmit their data 2042 from each set asset 2010, 2014,via their respective communication paths 2011, 2015, to their respectparent asset at the unit level 2000. As with the asset level, avalidation is conducted to assure all smart assets have been processedat the set level 2044 according to one implementation. It is to beunderstood that other implementation is possible and this type offunctionally may be implement for a sub-set of Integrated Smart AssetControl Systems.

Finally, all smart assets at the unit level 2000 are processed 2046. Inpreparation to develop the control to articulate the application to thederived real time operational profit point.

FIG. 21A is a block diagram and FIG. 21B is a logic flow diagramillustrating asset control communication structure from unit to set, andset to asset, of a Hierarchical Asset Control Application in accordancewith various embodiments of the present disclosure.

To determine operational or set point constraint parameters, all unitlevel smart assets 2100 are processed 2150 to determine operational setpoints or constraint parameters of each child asset at the set level ofthe hierarchy 2152. When complete, these unit smart assets 2100 transmittheir data 2154 via their respective communication paths 2111, 2115, totheir respective child asset at the set level 2110, 2114. A validationis conducted to assure all smart assets have been processed at the unitlevel 2156. It is to be understood that other implementation is possibleand this type of functionally may be implement for a sub-set ofIntegrated Smart Asset Control Systems.

Once complete, smart assets at the set level 2110, 2114 are processed2158 to determine their operational or set point constraint parametersfor their respective children smart assets 2160. In turn these set smartassets transmit their data 2162 from each set asset 2110, 2114, viatheir respective communication paths 2121, 2113, 2125, 2127 to theirrespect parent asset at the asset level 2120, 2122, 2124, 2126. As withthe set level, a validation is conducted to assure all smart assets havebeen processed at the set level 2164. Finally, when complete as allsmart assets at the asset level 2120, 2122, 2124, 2126 are processed2166 each level of the Hierarchical Asset Control Application will haveoperational or set point constraint parameters to operate at a real timeoperational profitability set point.

Small and Big Data Analytics

In some embodiments, an asset control system can include an analyticscomponent or engine for information management and analysis tocontinually optimize the performance of an industrial operation overtime. The analytics engine can work in parallel with the real timecontrol system but without the real-time constraints. FIG. 22 is adiagram depicting an analytics view of the asset control system inaccordance with some embodiments of the present disclosure. As shown,each CPS associated with an asset can utilize network connectivity todirectly or indirectly report data generated and/or received by theintelligent agent in the CPS (e.g., process histories, measured data,actions taken) to the big data analytics engine 2205. For example, insome instances the primary asset CPSs can report data directly to thebig data analytics engine 2205. In other instances, a unit/work cellasset set intelligent agent can collect data from the primary smartassets it is responsible for and report the data to the higher levelasset set or the big data analytics engine 2205. The big data analyticsengine 2205 can collect and process the reported data to extractinsights that can be used to optimize the operational efficiency and/orother control functions discussed above. In some embodiments, eachintelligent agent associated with a smart asset or smart asset set orother smart asset groupings can include a small data analytics engine(e.g., small data analytics engine 370 of FIG. 3B) that can locallycollect and analyze the data associated with the asset or asset set andreport results to the big data analytics engine 2205 directly or througha higher level intelligent agent.

4. Example Processing

FIG. 23 is a diagram illustrating an industrial process examples inaccordance with various embodiments of the present disclosure. Anexample exothermic reactor unit that is used for patent applications andother design documents.

A complete reactor unit 2300 is illustrated which includes a reactorvessel 2310 and a number of sensors and valve positioners. Pressureindicator 2310PI, temperature indicator 2310TI, and level indicator2310LI provide measurements inside the reactor vessel. A reactor coolingjacket 2311 with associated flow valve 2311FV controls the flow rate ofcooling water into the reactor cooling jacket 2311. A valve positioner2315FV controls the flow of warm water from the reactor cooling jacket2311 to the effluent of the plant and is monitored by a flow indicatorsensor 2315FI and temperature indicator 2315TI to provide measurement ofthe temperature and flow of the effluent leaving the reactor unit 2310.

The reactor agitator 2312 stirs the reagents to ensure completereaction. “Product C” has an associated pump 2313 and flow valve 2313FV.An emergency reaction quench tank 2314 and associated flow valve 2314FVis available to stop the reaction and will solidify reactants and renderthe reactor vessel 2310 unusable.

A heat exchanger 2320 that controls the temperature of the cooling waterpumped to the reactor jacket 2311 is associated with a series of sensorsand valve positioners. Temperature indicator 2320TI, flow indicator2320FI, and valve positioner 2320FV measure and maintain the chilledwater to the heat exchanger 2320. The fresh water pump 2340 pumps freshwater into the heat exchanger. This fresh water is what is pumped intothe reactor jacket 2311 to remove heat from the reactor vessel 2310. Avalve positioner associated with this fresh water pump 2340 controls theflow rate of the cooling water entering the heat exchanger 2320.

“Reagent A” pump 2350 pumps the reagent material into the reactor vessel2310. Related to the pump are a flow sensor 2350FI and a valvepositioner 2350FV. A similar structure exists for “Reagent B” where apump 2360 pumps the reagent into the reactor vessel 2310. Related to thepump are a flow sensor 2360FI and a valve positioner 2350FV.

A pressure relief valve 2370 vents open when the pressure inside thereactor vessel 2310 exceeds a limit. This vents to a smoke stack whichis open to the outside environment.

In the present example a chiller and reservoir 2380 supply is used tocool the public water to a desired temperature and pump 2385 into thereactor cooling jacket 2311. Further, in abnormally high temperatureconditions, it may be pumped directly into the reactor cooling jacket2311 to quickly slow or stop a reaction while preserving the ReactorVessel. This chill water may also be added directly to the effluent toreduce the BTUs being released to the environment.

FIG. 24 is a diagram illustrating an industrial process examples inaccordance with various embodiments of the present disclosure. Anexample exothermic reactor unit that is used for patent applications andother design documents. As one of many examples, the exothermic reactor2310, reactor jacket 2311, temperature indicator 2310TI, and 2310LI areequipment that comprise a single asset, named the reactor asset. Othersmart assets exist as illustrated in FIG. 23 and have been identifiedand characterized as part of an Integrated Smart Asset Control System,which for example purposes include a chiller asset 2380, heat exchangerasset 2320, reagent A 2350 and B 2360, product C 2313, emergency quench2314, agitator 2312, and effluent 2315 asset. It is to be understood forthe purposes the following examples as illustrated by the figures, thefeatures and functionality are achieved and executed by an operationalIntegrated Smart Asset Control System developed from a HierarchicalAssets Control Applications and corresponding control hardware developedfor the underlying equipment elements discussed with regards to FIG. 23.

As part of the ongoing real time optimization of the entire HierarchicalAsset Control Application, constraints for safety 2410, reliability2420, and environment 2430 are determined on each individual asset.Parameters for each asset constraint are derived by a user or the systemas described above and are specific to the equipment which composes theasset, application, and real time and historic data. As one of manypossible examples, environmental constraints for the water output to theeffluent may require per EPA guidelines a temperature range of 35 to 65degrees Celsius and a maximum of 5 gallons per minute. All constraintsare derived at the asset level for the reactor asset, and all othersmart assets in the application. As detailed in FIGS. 20A and 20B, eachindividual asset constraint set is derived and transmitted to theirparent asset, until the unit level asset.

An optimal objective point is input to the system. An example may be toproduce 10,000 pounds of Product C per day utilizing a continuousoperation. For the present example the objective to be optimized isprofit which can be calculated knowing various parameters such as thevarious parameters regarding operational overhead and the profit perpound of Product C.

In addition to the objective to be optimized, constraint limits foroptimal values are input into the system. The current operation pointand the current constraints have now been derived. Each constraint andobjective known to the system, a normalization of all constraints andobjectives is necessary to perform an optimization. Once thisnormalization is complete, a zone of application operation will beknown, as will the optimization point for the application at theparticular point in time the measurements were taken from theapplication.

With the present state and the optimal state now known an optimizationcan be derived. FIG. 25 is a diagram illustrating various examples ofdetermining an optimization of a hierarchical industrial asset set inaccordance with various embodiments of the present disclosure.

Once all the constraints have been derived and input into theHierarchical Asset Control Application 2510 based on real timemonitoring of the complete application, and normalized 2520 as describedin FIGS. 18A and 18B, the current state, process operation area, andoptimal state are known and an optimization for the given constraintsand objectives can be derived 2530.

FIG. 26 is a diagram illustrating various examples of adjusting ahierarchical industrial asset set to effect the change of theapplication to the optimal point of operation. In the present example, aderived optimization may include, introducing chilled water from thechiller 2380 asset to the reactor jacket 2311 as the temperature in theexothermic reactor is too high to meet the environmental constrains, perthe temperature indicator 2310TI. Further, the agitator 2312 asset maybe activated on the reactor jacket 2311 effluent purge via the flowvalve 2315FV to further assure the effluent temperature is within theconstraint range. Finally, to meet the gallons per minute requirement,both the temperature 2315TI and flow rate 2315FI will be monitored andthe effluent closed via the valve 2315FV as needed.

An optimized characteristic has been derived 2610 and is transmittedthroughout the smart assets, groups of smart assets or other smart assetgroupings. From the example above a control optimization to theIntegrated Smart Asset Control System 2620 may include a control to pumpchill water 2385 at a rate of 5 gallons per minute, while opening thereactor jacket flow valve 2311FV to allow the warmer reaction jacketwater to purge through the associated flow valve 2115FV. Thisoptimization may, for example, be allowed to continue for a period oftime and verified optimal 2630 through the real time analysis of thecomplete Integrated Smart Asset Control System.

In this way, as is contemplated in embodiments of this disclosure anIntegrated Smart Asset Control System may operate and be optimized inreal time for any selected set of objectives and constraints while theindustrial process operator, business, or ever evolving industrydetermines beneficial.

5. Example Features or Aspects of the Asset Control System

Various example features or aspects of the asset control system inaccordance with some embodiments of the present disclosure are providedbelow.

In some embodiments, the asset control system provides a full autonomousor semi-autonomous control system for each asset within industrialplants.

In some embodiments, lower level equipment asset control systems arepreconfigured by equipment suppliers.

In some embodiments, automatic control system of systems configured asavatars or intelligent agents at lower levels automatically connect withand morph into avatars at the higher levels.

In some embodiments, configuration is simplified with configuration ateach level only involving the specific functionality at that level.

In some embodiments, control from real-time process control forefficiency improvement is extended to real-time process control forefficiency, reliability, safety risk, environmental risk, security riskand profitability improvements.

In some embodiments, system-ness is enforced at each upper level avataror intelligent agent.

In some embodiments, each intelligent agent provides a full complementof functionality associated with the level of asset or asset setassociated with the intelligent agent, including, but not limited to:monitoring, contextual analytics, asset performance control, assetoptimization, asset security, and/or asset history (operational,maintenance, performance).

In some embodiments, dynamic business processes (profitability etc.) aretreated in the same manner as dynamic physical processes.

In some embodiments, granularity of control is reduced to one, and sucha single loop granularity gives unprecedented scalability.

In some embodiments, the asset control system has single loop integrityand any failure needs only a single loop back-up.

In some embodiments, power needs are provided by energy harvesters whichcan be a component of the smart asset or CPS. The energy harvesters canextract energy from the ambient environment (e.g., vibration).

In some embodiments, the asset control system is self-identified andconfigured. In the asset control system, each equipment asset can beprovided with a CPS and associated intelligent agent by the equipmentsupplier. When the intelligent agent is connected into a larger assetcontrol system it provides a logical and unique intelligent agentidentifier which identifies it to the system.

In some embodiments, control and asset management are embedded withinthe actual asset, and not “stuck-on” as is typical for traditionalprocess control.

In some embodiments, the physical process devices or smart assets modelthemselves.

There is no artificial equipment model that is needed to mimic the smartassets. The smart assets model themselves.

In some embodiments, the asset becomes as “controlled” as an asset asprocesses normally are.

In some embodiments, the necessary sensing/measurement are combined withcontrol, output/actuation and asset management in same resource in theasset itself.

In some embodiments, control and asset management are “clustered” (in acommon communication “fog”) around clusters of process devices as theynaturally exist.

In some embodiments, the device vendors provide control algorithms andasset performance management for their equipment smart assets. Dumbprocess devices or smart assets can be given intelligence to bothcontrol itself and monitor its own health. This can apply to devices orsmart assets as dumb as a simple length of pipe. In some embodiments,any asset can be given this intelligence via the techniques disclosed inthe present disclosure.

In some embodiments, “control” is sold with the equipment itself, not“added-on.”

In some embodiments, at least the intelligent agent can be downloaded tothe smart assets later on.

In some embodiments, asset performance intelligence is provided by thevendor.

In some embodiments, wiring for connectivity as well as networking iscompletely eliminated.

In some embodiments, the definition of “control” is extended tooperational control to set and meet financial forecast metrics just likea control loop except that business value performance metrics arecontrolled, not just process parameters.

In some embodiments, control is distributed to devices or assets and ispowered by harvested power, communicating on a wireless cloud ofcombined meshes, operating within a unifying systems framework.

In some embodiments, control is no longer “stuck on.” It is in situ,part of the process.

In some embodiments, a plant models itself and the control representsthe actual plant.

In some embodiments, the asset control system provides maximumreliability and resiliency at lowest possible cost.

In some embodiments, control is extended from the process to thebusiness.

In some embodiments, the Internet of Things is applied to industrialprocess control for its functionality, not just connectivity.

6. Computer Systemization

FIG. 27 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

In the example of FIG. 27, the computer system 2700 includes aprocessor, main memory, non-volatile memory, and an interface device.Various common components (e.g., cache memory) are omitted forillustrative simplicity. The computer system 2700 is intended toillustrate a hardware device on which any of the components and methodsdepicted in this disclosure. For example, the processor unit 1400, 1500,1600, 1700, 1800 depicted in FIGS. 14A, 15A, 16A, 17A, 18A respectivelycan be a hardware device such as a processor or a computer systemcapable of performing computations, calculations, processing, and/or thelike to perform the tasks described herein. The computer system 2700 canbe of any applicable known or convenient type. The components of thecomputer system 2700 can be coupled together via a bus or through someother known or convenient device.

The processor may be, for example, a conventional microprocessor such asan Intel Pentium microprocessor or Motorola power PC microprocessor. Oneof skill in the relevant art will recognize that the terms“machine-readable (storage) medium” or “computer-readable (storage)medium” include any type of device that is accessible by the processor.

The memory is coupled to the processor by, for example, a bus. Thememory can include, by way of example but not limitation, random accessmemory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). Thememory can be local, remote, or distributed.

The bus also couples the processor to the non-volatile memory and driveunit. The non-volatile memory is often a magnetic floppy or hard disk, amagnetic-optical disk, an optical disk, a read-only memory (ROM), suchas a CD-ROM, EPROM, or EEPROM, a magnetic or optical card, or anotherform of storage for large amounts of data. Some of this data is oftenwritten, by a direct memory access process, into memory during executionof software in the computer 2800. The non-volatile storage can be local,remote, or distributed. The non-volatile memory is optional becausesystems can be created with all applicable data available in memory. Atypical computer system will usually include at least a processor,memory, and a device (e.g., a bus) coupling the memory to the processor.

Software is typically stored in the non-volatile memory and/or the driveunit. Indeed, for large programs, it may not even be possible to storethe entire program in the memory. Nevertheless, it should be understoodthat for software to run, if necessary, it is moved to a computerreadable location appropriate for processing, and for illustrativepurposes, that location is referred to as the memory in this paper. Evenwhen software is moved to the memory for execution, the processor willtypically make use of hardware registers to store values associated withthe software, and local cache. Ideally, this serves to speed upexecution. As used herein, a software program is assumed to be stored atany known or convenient location (from non-volatile storage to hardwareregisters) when the software program is referred to as “implemented in acomputer-readable medium.” A processor is considered to be “configuredto execute a program” when at least one value associated with theprogram is stored in a register readable by the processor.

The bus also couples the processor to the network interface device. Theinterface can include one or more of a modem or network interface. Itwill be appreciated that a modem or network interface can be consideredto be part of the computer system. The interface can include an analogmodem, ISDN modem, cable modem, token ring interface, satellitetransmission interface (e.g., “direct PC”), or other interfaces forcoupling a computer system to other computer systems. The interface caninclude one or more input and/or output devices. The I/O devices caninclude, by way of example but not limitation, a keyboard, a mouse orother pointing device, disk drives, printers, a scanner, and other inputand/or output devices, including a display device. The display devicecan include, by way of example but not limitation, a cathode ray tube(CRT), liquid crystal display (LCD), or some other applicable known orconvenient display device. For simplicity, it is assumed thatcontrollers of any devices not depicted reside in their respectiveinterface.

In operation, the computer system 2700 can be controlled by operatingsystem software that includes a file management system, such as a diskoperating system. One example of operating system software withassociated file management system software is the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. Another example ofoperating system software with its associated file management systemsoftware is the Linux operating system and its associated filemanagement system. The file management system is typically stored in thenon-volatile memory and/or drive unit and causes the processor toexecute the various acts required by the operating system to input andoutput data and to store data in the memory, including storing files onthe non-volatile memory and/or drive unit.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing devicethat manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission, or display devices.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods of some embodiments. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the techniques are not described withreference to any particular programming language, and variousembodiments may thus be implemented using a variety of programminglanguages.

In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a laptop computer, a set-top box (STB), apersonal digital assistant (PDA), a cellular telephone, an iPhone, aBlackberry, a processor, a telephone, a web appliance, a network router,switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine.

While the machine-readable medium or machine-readable storage medium isshown in an exemplary embodiment to be a single medium, the term“machine-readable medium” and “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresently disclosed technique and innovation.

In general, the routines executed to implement the embodiments of thedisclosure, may be implemented as part of an operating system or aspecific application, component, program, object, module, or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processing units or processors in acomputer, cause the computer to perform operations to execute elementsinvolving the various aspects of the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable (storage) media include but are not limitedto recordable type media such as volatile and non-volatile memorydevices, floppy and other removable disks, hard disk drives, opticaldisks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital VersatileDisks, (DVDs), etc.), among others, and transmission type media such asdigital and analog communication links.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, shall referto this application as a whole and not to any particular portions ofthis application. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above detailed description of embodiments of the disclosure is notintended to be exhaustive or to limit the teachings to the precise formdisclosed above. While specific embodiments of, and examples for, thedisclosure are described above for illustrative purposes, variousequivalent modifications are possible within the scope of thedisclosure, as those skilled in the relevant art will recognize. Forexample, while processes or blocks are presented in a given order,alternative embodiments may perform routines having steps, or employsystems having blocks in a different order, and some processes or blocksmay be deleted, moved, added, subdivided, combined, and/or modified toprovide alternative or sub combinations. Each of these processes orblocks may be implemented in a variety of different ways. Also, whileprocesses or blocks are at times shown as being performed in series,these processes or blocks may instead be performed in parallel, or maybe performed at different times. Further any specific numbers notedherein are only examples: alternative implementations may employdiffering values or ranges.

The teachings of the disclosure provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various embodiments described above can be combined toprovide further embodiments.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the disclosure can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further embodiments of thedisclosure.

These and other changes can be made to the disclosure in light of theabove Detailed Description. While the above description describescertain embodiments of the disclosure, and describes the best modecontemplated, no matter how detailed the above appears in text, theteachings can be practiced in many ways. Details of the system may varyconsiderably in its implementation details, while still beingencompassed by the subject matter disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the disclosure should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the disclosure with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the disclosure to the specific embodimentsdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe disclosure encompasses not only the disclosed embodiments, but alsoall equivalent ways of practicing or implementing the disclosure underthe claims.

From the foregoing, it will be appreciated that specific embodimentshave been described herein for purposes of illustration, but thatvarious modifications may be made without deviating from the spirit andscope of the embodiments. Accordingly, the present disclosure is notlimited except as by the appended claims.

We claim:
 1. A method of Hierarchical Asset Control Application processdevelopment, performed by a system controller, comprising: accessing, anequipment list; determining, an input equipment list and associatedparameters; selecting, an intelligent asset template from an intelligentasset template library to instantiate an intelligent agent for anequipment element contained in the input equipment list; creating, aninstantiation of an intelligent asset for the selected equipmentelement; populating, the selected template with operational constraintsand operational objects for the selected equipment element; anddeveloping, an associated hierarchical arrangement of operationallyoptimizable asset control relationships for a Hierarchical Asset ControlApplication by connecting each of the instantiated intelligent agentsbased on parent/child information; wherein corresponding controlhardware requirements are based on the developed Hierarchical AssetControl Application; and wherein the developed Hierarchical AssetControl Application and the corresponding control hardware requirementsare integrated with the equipment elements to create an Integrated SmartAsset Control System.
 2. The method of claim 1, wherein the populatedselected intelligent asset template includes intelligent agentinstantiation information.
 3. The method of claim 1, whereincommunication/control pathway validation is performed for theHierarchical Asset Control Application.
 4. The method of claim 1,wherein simulation is performed on the Hierarchical Asset ControlApplication to determine anomalous conditions during operation.
 5. Themethod of claim 4, wherein simulating the Hierarchical Asset ControlApplication involved generating virtualized equipment element data andexecuting process control elements.
 6. The method of claim 1, whereinthe Integrated Smart Asset Control System includes more than one smartasset control level.
 7. The method of claim 1, further comprisingaggregating one or more intelligent asset templates to instantiate anintelligent agent for incorporation with a smart asset.
 8. The method ofclaim 1, further comprising instantiating an intelligent asset templatefor a smart asset grouping for an intelligent asset template isinstantiated for a smart asset set.
 9. The method of claim 1, whereinthe intelligent asset template is configured to include applicationspecific data.
 10. The method of claim 1, further comprising determiningasset operational library type and industry specific hierarchicalcontrol application default requirements.
 11. The method of claim 1,wherein the intelligent asset template is developed as an intelligentagent for the particular equipment element control model.
 12. The methodof claim 1, wherein the intelligent asset template includes dataparameters including, suggested asset interconnects with assets,operational constraints, operational objectives, highavailability/criticality parameter, or industry specific industrialapplications.
 13. The method of claim 12, wherein the intelligent assettemplate includes vendor device-specific model information.
 14. Themethod of claim 12, wherein, the intelligent asset template includesoperational parameters from generic device type models.
 15. The methodof claim 1, further comprising determining operational constraintparameters including reliability, environmental, or safety.
 16. Themethod of claim 1, further comprising determining operational objectiveparameters including energy cost, materials cost, production value, orprofitability.
 17. The method of claim 1, further comprising determiningoperational efficiency parameters.
 18. The method of claim 1, whereinconnecting iteratively includes grouping related smart assets into smartasset groupings that define parent/child control relationship acrosssmart assets.
 19. A system of Hierarchical Asset Control Applicationprocess development, performed by a system controller, comprising:accessing, with a processor, an equipment list; determining, with theprocessor, an input equipment list and associated parameters; selecting,with the processor, an intelligent asset template from an intelligentasset template library to instantiate an intelligent agent for anequipment element contained in the input equipment list; creating, withthe processor, an instantiation of an intelligent asset for the selectedequipment element; populating, with the processor, the selected templatewith operational constraints and operational objects for the selectedequipment element; and developing, with the processor, an associatedhierarchical arrangement of asset control relationships for aHierarchical Asset Control Application by connecting each of theinstantiated intelligent agents based on parent/child information;wherein corresponding control hardware requirements are based on thedeveloped Hierarchical Asset Control Application; and wherein thedeveloped Hierarchical Asset Control Application and the correspondingcontrol hardware requirements are integrated with the equipment elementsto create an Integrated Smart Asset Control System.
 20. The method ofclaim 19, wherein the populated selected intelligent asset templateincludes intelligent agent instantiation information.
 21. The method ofclaim 19, wherein communication/control pathway validation is performedfor the Hierarchical Asset Control Application.
 22. The method of claim19, wherein simulation is performed on the Hierarchical Asset ControlApplication to determine anomalous conditions during operation.
 23. Themethod of claim 22, wherein simulating the Hierarchical Asset ControlApplication involved generating virtualized equipment element data andexecuting process control elements.
 24. The method of claim 19, whereinthe Integrated Smart Asset Control System includes more than one smartasset control level.
 25. The method of claim 19, further comprisingaggregating one or more intelligent asset templates to instantiate anintelligent agent for incorporation with a smart asset.
 26. The methodof claim 19, further comprising instantiating an intelligent assettemplate for a smart asset grouping for an intelligent asset template isinstantiated for a smart asset set.
 27. The method of claim 19, whereinthe intelligent asset template is configured to include applicationspecific data.
 28. The method of claim 19, further comprisingdetermining asset operational library type and industry specifichierarchical control application default requirements.
 29. The method ofclaim 19, wherein the intelligent asset template is developed as anintelligent agent for the particular equipment element control model.30. The method of claim 19, wherein the intelligent asset templateincludes data parameters including, suggested asset interconnects withassets, operational constraints, operational objectives, highavailability/criticality parameter, or industry specific industrialapplications.
 31. The method of claim 30, wherein the intelligent assettemplate includes vendor device-specific model information.
 32. Themethod of claim 30, wherein, the intelligent asset template includesoperational parameters from generic device type models.
 33. The methodof claim 19, further comprising determining operational constraintparameters including reliability, environmental, or safety.
 34. Themethod of claim 19, further comprising determining operational objectiveparameters including energy cost, materials cost, production value, orprofitability.
 35. The method of claim 19, further comprisingdetermining operational efficiency parameters.
 36. The method of claim19, wherein connecting iteratively includes grouping related smartassets into smart asset groupings that define parent/child controlrelationship across smart assets.
 37. A method of Hierarchical AssetControl Application process development, performed by a systemcontroller, comprising: accessing an equipment list; selecting anintelligent asset template from an intelligent asset template library toinstantiate an asset application model for an equipment elementcontained in the input equipment list; populating the selected templatewith operational constraints and operational objects for the selectedequipment element; developing, with the processor, an associatedhierarchical arrangement of asset control relationships for aHierarchical Asset Control Application by connecting each of theinstantiated intelligent agents based on parent/child information; andwherein the selected and populated intelligent asset template includesintelligent agent instantiation information; wherein correspondingcontrol hardware requirements are based on the developed HierarchicalAsset Control Application; and wherein the developed Hierarchical AssetControl Application and the corresponding control hardware requirementsare integrated with the equipment elements to create an Integrated SmartAsset Control System.